text:
Funding for applied mathematics postdoc - how does it work?
I am an applied mathematics Ph.D. student at the end of my Ph.D., looking for postdoc opportunities. My strategy has been to apply for different positions that are already funded. Now I am thinking about applying for funding myself.  It seems like a good way to gain independence as a researcher. But I am not sure how to go about it.  Here is how I think it works:

Send research proposals to different research groups, asking if they would be interested in the problems and providing a postdoc position if I can get funding.
If one of them agrees, I apply for funding. I suppose that it is normal to apply for the same funding at several places (?).
If I am lucky enough to get a grant, I then contact the research group and get the "position"

Is this the correct way to go about it?

and geographically where are you aiming?

Europe, North America and Australia

If you want to fund your own position, probably the best course of action would be to look for the various postdoctoral fellowships awarded by organisations like the NSF, the European Union, and many other national science funding bodies. Your advisor may know some examples that would be most appropriate for you at this career stage. Each fellowship of course has their own rules, so you would need to study them carefully. Generally, the fellowships at this stage would support yourself through either a stipend or by providing your institution with money for your salary. They may or may not pay overheads. In addition, there may be a modest research budget, which for theoretical fields typically can be used to cover for example conference travels.
In case of the fellowships that I am personally aware of, you would have to mention a host instution at which you would want to carry out your research. Typically you would have to provide a signed letter of commitment from the institution, agreeing to hire/support you if your fellowship would be awarded. So indeed it is important to get in touch with possible institutions. You would usually also have to argue in your application why this place would be a good fit for the project.
So how to go about once you have found suitable funding sources? As you said, it is indeed a good idea to contact professors at those universities that would be a good fit for the project that you have in mind. Your advisor might be able to help here as well in suggesting and/or contacting the right people. At this stage, a full research proposal is not really necessary yet, but you should be able to explain briefly what you are interested in, and how it fits in with the group.
Once you have found someone to support you, you can work on your proposal according to the guidelines for the fellowship. Your prospected host instution may provide some feedback on the proposal, but in my experience this is more common only for bigger grants at later career stages. In my experience, professors are generally happy to support your application, since it is relatively low risk for them. But be sure to secure their support in time, since it may take some time to navigate the bureaucracy to get a letter of commitment, for example.
You can only apply for each fellowship once per round, but you can apply with a similar proposal to different fellowships. In some applications you will be required to disclose this, however. And almost certainly, if you would get awarded more than one fellowship, you would have to choose between them since you cannot get funded for the same research twice.
Since typically the institution has to provide a letter of commitment, once you are awarded a fellowship they will be obliged to hire you (or support you, in terms of office space and such) for the duration of the project. At this point it will be mainly up to the adminstration of the university to proceed and prepare the relevant paperwork.
Good luck!


text:
How exhaustive should I be when enumerating works in a State of the Art
I'm currently writing my PhD thesis' State Of The Art.
I have subdivided the field of study. My problem is that I found my self on many occasions writing paragraphs as follows:

'Several authors has proposed to use the K-POP method to enhance the
  egnasticity of the computations by solving the Muffin
  equations. In [1] statistical values are used with X improvements.
  Smith [2] proposes a Montercarlo solution for Muffin with this and
  that results. Also...

And then I realize that there are at least 20 different ways to solve Muffin equations. I could further subdivide the field, but I don't want because this won't really contribute much to my SOA, because all I need to point out is that egnasticity can be improved using a solution to Muffin equations.
At this point I wonder: How exhaustive should I be when enumerating all the works of a given area that I don't want to subdivide any further? Should I describe all? Should I describe some and cite all? Should I describe some and cite some?
Note: needles to say 'egnasticity' or 'Muffin equations' are silly names for the example and the K-POP is usually used for different purposes than research.

It is difficult to give a general answer to "how much detail do you need to summarize", since that depends on a lot of things, but you should first of all ask your supervisor what they expect. That is always the definitive answer to "how much is enough" regarding a thesis: it doesn't really matter what answers you might get at Academia SE; what really matters is what your supervisor (and thesis committee) accept.
That said, if your goal is to provide the most useful information for researchers that might read your thesis, then I would suggest that you be as exhaustive and detailed as possible. If you have gone through the trouble to read these articles (at least enough to actually understand the differences between various Muffin equations), then it would be very helpful to others for you to spare them the read by summarizing what you have read and understood.
From what you describe, though, I don't think writing a textual paragraph would be the most helpful way to describe this information for your readers (nor the easiest for you). I recommend that you present these details in a table which cuts out all the fluffy words and just gets to the point: the name of each variation; the citation of the article that presents it; the key details that distinguishes it from other variations; the pros and cons. In addition, if you could group the 20 variations into three to six categories that are similar to each other, that would be a further helpful layer of synthesis.
There are two further advantages of using a table:

For yourself, a table helps you focus on what is essential in each article. By carefully thinking about the structure of the table (mainly, which columns are necessary), you help yourself to better understand what you're reading and to focus on what you consider important.
You don't need to worry about which ones are "worth" summarizing or in how much detail: you just simply summarize everything, briefly and to the point. That said, if a few variations stand out to you as important, then by all means write as many paragraphs as you want to that discuss these ones in detail. But then you won't need to do that for any of the others that you have already briefly summarized.

source: Stack Exchange

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/

sort: votes


text:
Can a lecturer force you to learn a specific programming syntax / language?
My class is currently further learning our relational database / SQL as part of the course.
The lecturer we have currently is restricting us to learning specifically Oracle SQL syntax rather than allowing us an option to using PostgreSQL, MySQL, SQLite, etc.
When we brought up our preference in the use of alternative systems due to working with those mentioned above in work placements or academic study from the year prior, we were talked down to for even discussing the matter.
Is this permissible within the UK to restrict students from using a preferred database syntax / software environment?

Edit to add: thank you for your insights and feedback. The post was made on behalf of myself and half a dozen class members who did not know the best route to address the matter, but now have a clearer understanding thanks to this community. For what it's worth, I can confidently say myself and my fellow students are confident with Oracle, MySQL and other SQL Basics from prior education, projects or employment and don't have an issue learning something new to keep progressing.

Comments are not for extended discussion; this conversation has been moved to chat.

No, they can't. But they can make you fail the class.
Lecturers usually have free rein over the specifics of the material taught. Therefore they are free to choose any technology or dialect as long as it is within the area of the course.
Nobody can force you to study anything but if you want to pass his exam or the course by e.g. submitting lab problems, you have to play by their rules.
They might not know any other dialect and therefore be only able to teach (and grade) Oracle SQL. Or they might consider it the best dialect, e.g., because of wide industry use.
Finally, although I would be annoyed by this as well, sometimes you have to suck it up and just pass the course. Lecturers that teach the same (obsolete) material for 20 years are unfortunately a reality in academia. At least you might learn what to avoid in the future.

Comments are not for extended discussion; this conversation has been moved to chat.

Unless there is something in the course description saying otherwise, the lecturer can choose the syntax for all submitted coursework and exams. Picking a specific syntax makes the lectures less confusing, as well as simplifying testing and grading of coursework and exams. To pass the course, you will have to learn Oracle SQL.
You may be confusing "requiring you to learn X" with "restricting you from learning Y". In the long term, you will have to learn a lot of material during your career, and it will not always be feasible to get classes in exactly what you need to learn. Independent study can become essential.
Consider repeating some of the coursework using the database of your choice, for your own education.

I'd like to add to Patricia's excellent answer by saying that if you end up doing this for a living, you will need to develop the ability to move between similar languages and frameworks to survive.  SQL, in particular, is not a technology to be precious about, because it's used everywhere.  Also, the amount of extra effort on your part is marginal compared to the amount of work the instructor would have to do if everyone picked their own version.  That's insane.  It would be like a business that let each dept use their own version of SQL, and then trying to normalize that into a single DB.

Rephrased a bit, your question says:

Can a lecturer base their course on a specific dialect of a tool?

I can and I do.

But I'd like to highlight a bit different aspect of this. A proper university-level lecture is (almost) never about concrete tools and languages. It's about concepts.
I am teaching Functional Programming this semester. The lecture material is telling about Haskell. The exercises are in Haskell. If someone would submit them in ML, they'd surely fail. But the concepts of functional programming is what this lecture actually is about. And concepts do not change.
I fully expect the students to be able to program in ML, Swift or what not, as long as it's a functional programming language and they have adapted to the syntax. Surely, they'd miss some concepts. Surely, they'd have some concepts they did not encounter in the lecture. But the basic approach, the generic mindset, and most of the concepts will still be there. And this is what my course is actually about.
In fact, they might never write a single line of Haskell in their life after they have got their grade. And it's still make them better programmers and computer scientists.

I can basically replace words and get the same statement about relational databases. The fact that you learn Oracle should have little impact on your ability to use MySQL (or what not) later in your life. Quite a number of large companies use Oracle anyway. Perhaps your lecturer is more fluent in Oracle than in something else, or maybe your department got that big shiny Oracle licence that'd be a shame not to use, or something completely different. The reason is irrelevant, you have to learn Oracle.
What you actually learn is foundations of relational databases. These foundations apply to all the *SQL things and even some that are not. Everything else is a minor adaptation to syntax and local quirks, which is not a subject of university education.

The fact that you learn Oracle should have little impact on your ability to use MySQL that very true. You have to use some existing language and each implementation can have its differences. Still, if you know how to write queries in any SQL dialect, you'd be able to write them in any one dialect. A table would function the same in Oracle, PostgreSQL, MySQL, etc, when it comes to selects and updates, and so on. Some special cases could differ but in the real world you not always have a choice in DB vendor, you'd likely be required to just use X and Y whereas you only know Z.

As educators, we like to lay claim to this "concepts not specific technologies" belief, but I don't see much evidence that's true. My C++ students don't feel comfortable about Java, even though I tell them at every turn what 95% is the same and what 5% is different. The 4-year college that most of my 2-year students transfer to doesn't accept a C++ class as satisfying a Java programming requirement. Job postings will require distinctly one, not the other. Etc.

Apple and google divide the market and use primarily non cooperative strategies. Microsoft has been responsible for the affordable pc, before hand a personal computer from Macintosh etc was a luxury. Now anyone can have full access and own a fully programmable computer (as opposed to android/apple smart device which is not fully programmable). How can anyone rationally deny this? And not only this, Windows also finds the optimal solutions ad well. Not sacrificing performance, compatibility, or affordability.

The windows 95-windows 10 era of Microsoft are really a modern marvel and now with the rise in other o.s. Companies such as apple and  google, it is becoming more and more clear how lucky we all have been. It is my fear that this era is curtailing in a change to a more regular times analogous to before the 1980s. That said academia never really used windows for study ironically, it's always relegated to the background and out of site.

Now I'm sure companies will pick appart every aspect of the 95-win10 o.s. And copy it. But the point is the free market enterprise started by Gates and Allen which was capable of accomplishing this will be forever gone.

The discussion seems to side-track. Technologies erode. Remember J#? Perl? Tru64 UNIX? VAX? The problem of teaching "Programming in XY" is that people are lost and have no job, when XY is deprecated.

@DanielR.Collins: Once upon a time I successfully got a C# job, but two weeks before starting I knew only C++ and Java. It worked out. Hence, concepts, yeah.

"Job postings will require distinctly one, not the other.": But this only tells how shallow some recruiters can be. An experienced programmer who masters, say, C++, Python and Haskell, won't find it difficult to pick Java or C#. Yes, it will take some time, but I would prefer to give a Java position to such a programmer than to someone who has only programmed in Java throughout their career.

This one of the best answers for me, because it directly addresses the notion that university education is about getting a student to understand an overarching principle rather than a specific implementation. By getting caught up in the specifics of having to remember LIMIT vs TOP, or thinking that (outer) joins must be done like WHERE table.col = othertable.othercol(+) (shudder at the thought of this, if it's happening) they are losing sight of the bigger picture that they're there to learn about storing and linking related data together and considering it in sets rather than row by row

@DanielR.Collins - Pragmatically speaking, all major DBMS should support ANSI SQL and then their own syntactic idiosyncrasies are layered on top of that. However, most employers tend to just list the tools they use and fail to really appreciate that. Unless you are doing detailed tuning of queries or making heavy use of platform specific features, if you are competent at ANSI SQL you can write competently on most DBMS and just look-up functions you need.

"It's about concepts."  This is just so true and so accurate.  I learned structured programming by learning BCPL.  What I learned in that course made it relatively easy to self teach myself C, since it was also available on the PDP-11/34A we had in the lab at that time.  But the basic concepts I learned from BCPL made the transition to C fairly trivial.  It wasn't until later that I found out that C was actually the grandchild of BCPL. :)

@dgnuff: As Hardy put it on a different, but related manner: "... because languages die and ideas do not". ;)

Yes, all of us as academics have anecdotes of understanding concepts and transitioning independently to new languages. So do I. But from what I see of student and HR populations I'm skeptical that's generally the case. I would like to see evidence that the percentage of people who do that without specific training in new technology is significant. If that were the case, the whole professional training industry would not exist.

Can a lecturer force you to learn a specific programming syntax / language?

Of course they can. Teachers are generally in charge of designing their course in whatever way they think is best suited to reach the intended learning outcomes. In some cases (especially more project-oriented courses) this may mean leaving the choice of technology open to students, but for other courses strictly mandating what students should use may be preferable.
Further, while I won't comment on the usage of Oracle specifically, there still may be good reasons to not use what you have been using before. Particularly, learning a different SQL dialect can be a very valuable lesson in itself. It makes you understand better what exactly is part of SQL (the standardized language) and what is part of the specific implementation that Postgres uses.
Finally, practicalities often dictate that teachers mandate certain technological ground rules. I have often had the case that I strictly required students in large classes (400+ students) to adhere to specific technologies and even submission templates, because it was not acceptable from a grading time perspective that TAs would need to spend time getting a solution to run before they could start grading it. Clearly this is an extreme case, but you still need to understand that letting each student pick their own technology escalates quickly in terms of grading effort.
That all is to say, you can certainly address (for instance in the teaching evaluations) if/that you feel that the pedagogical goals of the course could be better reached when using a SQL dialect that you already know, but a formal complaint has no legs to stand on.

While I generally like the answer, I feel that by mentioning "best suited to reach the intended learning outcomes" in the first paragraph you give it the highest importance and I would argue that practicalities mentioned in the last paragraph are the main reason for the selection of a technology. That's what he knows, thats what he is used to.

@problemofficer This may or may not be true. I don't know your teacher.

Take a step back: How many students are in the class? I'll guess 100. Each student will have their own preferences -- different SQL dialects, different learning speeds and styles, different topics, different assessments, etc. The lecturer cannot accommodate all of these preferences, so there is a need to compromise.
It may be only 10% more work for the lecturer to accommodate your preferred SQL dialect. You may think that's reasonable. But if each student has something similar to accommodate, then suddenly the lecturer has 1000% more work. That's clearly not practical.
From the lecturer's point of view, it is essential to keep the course workload under control. If he or she begins accommodating requests, it can get out of hand very quickly. Being strict is a skill that lecturers have to learn.  If you want someone who tailors everything to your preferences, hire a private tutor.

Very true. And some of those 10% of additional workload can already be achieved by possibly endless discussions with a) students whose favourite dialect was admitted and who now complain they are getting less material and support than those who use the main dialect of the class, and b) students whose favourite dialect was not admitted and who point out that others got their favourite dialect admitted, too, why can't theirs be admitted, as well. (Source: Personal experience, not even related to dialects of the same language, but to different programming languages altogether.)

I took a File Organization and Processing class years ago and the Adjunct Prof said, "You can turn it in in any language system you want to use because I can read it. If I cannot, you're responsible for teaching me enough of it so I can grade it." There was a restriction against using a language which 'did it for you' (meaning you had to demonstrate knowledge of how to code the assignment... for example you couldn't use a .Sort feature to sort stuff).

@J.ChrisCompton 's comment reminds me of my thesis adviser.  He had an assignment to write an algorithm for matrix multiplication.  In the lab he discovered an APL keyboard.  The rest, is a one-liner... :-D

When I took a course in numerical methods eons ago, the first few assignments were to be done in Fortran 77. After doing some in Fortran, the professor then moved on to assignments in APL, SPITBOL, and C, each meant to demonstrate how different languages allowed problems to be attacked in different ways. No class time was spent in teaching these languages, we were expected to read the manuals and figure it out for ourselves in the week or so available. And, I learned a lot about a bunch of different languages and constructs that I still use today.
But, you can bet dollars to donuts that an assignment to do in APL was only accepted in APL. If it was supposed to be SPITBOL, you might get away with SNOBOL (although you really needed recursion) but Fortran wasn't going to fly.
Yes, for many reasons, a professor can define the parameters of assignments, including specifics such as the allowable programming language.

SNOBOL had some cool concepts, particularly for the early 1960s. Never used it since that course though.

I briefly used SNOBOL in the early '80s!

I was taught a bit of SNOBOL at University in 1971, and my, it made it much easier to grok XSLT when I encountered that in 1999.

@MichaelKay having never used SNOBOL but having melted my brain with XSLT at one point, I'm not sure if I should take your statement as an endorsement or condemnation of SNOBOL.

Well, both SNOBOL and XSLT have a processing model based on matching input against patterns and firing off appropriate rules based on which patterns match. All I'm saying is, if you've come across one language that uses that paradigm, it's very much easier to pick up another; while if you haven't come across that kind of language before, it might well melt your brain. The most powerful concepts in computing often take some mastering.

Fortran 77? Oh, we used to dream of writin' Fortran 77! Would ha' been the lap 'o luxury to us. We used to code in COBOL in an old water tank on a rubbish heap. We got woke up every morning by having a load of rotting fish dumped all over us!

While I liked a lot of things about SNOBOL, the program control structures really sucked.  I lost all interest in programming in it after my first assignment.

Another vote for SPITBOL, which I learned at college and have never had a chance to use since. That horrible mess of Perl that was the header of everyone's first CGI scripts would have been maybe 2 lines of SPITBOL. Can we start a campaign to revive it?

I have used many languages throughout my studies and career and you can be sure of a few things

In School, the teacher is always right, so either get on board or get off
There is never going to be a single perfect language to use that will be used by everyone everywhere (SQL vs mongoDB, C++ vs JAVA, the list goes on and on)
Learning different programming languages is a necessary skill for a programmer

It's great a professor is forcing students to use a less popular language or a language that is not preferred. It helps prepare you for the real world where you will come across a job/situation where you have to use a programming language you've never used before. The hurdles of learning a new language in school will help with the hurdles of learning a new language in your future job.

After having to use FORTRAN 77 and Perl 4 in a real job less than a year ago, I can vouch for the necessity of being able to pick up new languages quickly.

SQL vs MongoDB is way more different than C++ vs Java.  It's more like C++ vs Haskell.

Yes, I am in control of my classes and will decide what in your work is worth points and what is not. I'll tell you that up front, with plenty of time for you to drop.
Not only will I demand you use the syntax and language I am teaching (for my own grading ease and consistency of results, like performance timing), but I will demand you follow my code formatting rules for indentation, tab stops, limits on line length, variable names and how to case them, and commenting within your code to help me see what you have done and why you are doing it.
Because the butt-sore truth is, when you get into the real world, you will find that writing working code is not enough, your code needs to be maintainable and readable by others. And chances are, you are going have to read and debug TONS of other people's code, and you will appreciate this then.
Before I was a professor I was a division manager in charge of code for a public company. Newbies are often given testing and debugging tasks to get them familiar with the code and systems and to evaluate their expertise and productivity.
So first, IRL programmers move on to other companies, departments, or roles within the company. So not only do we have to deal with code by programmers no longer with us, the senior programmers are often working on deadline for critical NEW projects, and they don't have the time to debug their own code from years ago, or even answer questions about it.
That makes maintainability of code very important, because hardware and software libraries and operating systems and compiler / interpreter release change much faster than our code base, and break our systems. The code must be legible, easy to understand, and easy to follow. I don't want any "black box" code that just "works right" because when it stops working, I don't want to rewrite it to fix it.
So I enforce that in my classes too. Yes your code has to work, but that isn't the whole grade. You must use the tools I gave you, or you don't pass: Because real companies have standards, even down to the editor you use, and my assignments are an exercise in you using the tools given, not just getting the right answer.
You must also write code that obeys my code formatting and commenting guidelines, those are worth a letter grade. Because IRL if you gave me (your manager) that as a coder, I'd have to send it back to you for proper formatting, and there goes 1 or 2 days of your pay and productivity because you can't follow simple list of written directions.
The equivalent in the classroom is your grade, so -10 for mistakenly thinking that getting the right answer is all that matters to the company. How you got the answer is important, and Maintainability of your code after you are gone or no longer maintaining it is also very important.

@CaiusJard Yeah, I worked at a company where every work station was required to be identical, and kept that way by sysadmins. Same IDE, editor, debugger, compiler, OS, libraries, even the same icons in the same place on the screen. Even the directory structure was the same. So anybody could sit at any station and work, or help somebody find a bug or whatever. No exceptions for personal style or preference. But you get used to it, and uniformity and interchangeability does provide tangible benefits, especially for remote help: If my system and yours are identical, I can duplicate your problem.

What will you do for the final semester worthless class? It's too late to drop.

@Joshua Not my problem! When I tell you the requirements on Day 1 and Day 2, if you are terribly offended by them you still have plenty of time to drop without any grade or financial penalty, at least at the universities I have been at, as a student or professor. What you do instead is none of my business, or responsibility. If you want to graduate, stay in the class and do the work I give you. Just like real life: If you want the paycheck, you will do the work following the rules your boss sets, because to your boss, your work isn't worth your paycheck if you insist upon doing it your way.

Teaching involves more than just doing the lecture, you have to prepare the materials, be familiar with the content, prepare exercises, exams, etc.
How do you suppose the professor would do that without restricting the dialect? Or how can the professor be fair to the students without restricting it?
Example: "We favour Oracle, but we accept any other dialect". So the student that chooses to do it in pgsql would be at a disadvantage because the material was not prepared for it, the syntax is different, examples won't work, etc...
But then we add pgsql because it is well known. But it would make no sense to add pgsql and not use mysql too. When does it stop?

To be fair, there is a simple answer to that - if all material is prepared for Oracle and a student chooses, by their own will, to use something else, they have elected to be at a disadvantage.

Yes, but then the guy fails, blame it on the professor, ends up creating a ruckus. Sure, he would be wrong, probably, but any official complains would most likely need to be thoroughly investigated, this can get really messy. Easier to nip it in the bud. Further, how would the professor mark the exams? (the "funcionality only", "black box", approach could work, but I rather double check what the students did)

@FábioDias  I would even argue that the "black box" approach wouldn't actually work, as programming (even SQL) is as much about the code as it is the result. Things like formatting, style, architecture, best practices, etc. are all part of learning a new language or technology, so not grading on them is a huge disservice to your students.

Hmm, I don't wish to be rude, but this seems like a rather bizarre question. It's like saying, "The professor is insisting we work from textbook X. But I'd prefer to use textbook Y." It may be that you could make a good case that Y is a better textbook. But if the class is going to be coherent, all the students should be working from the same textbook.
If everyone used a different dialect of SQL, then the professor would have to include discussions of all the different dialects in every class. Not only would the prof have to know all the dialects and all the differences, but he would have to take class time to discuss these differences constantly, rather than concentrating on the basic points. For students who are already thoroughly familiar with SQL, discussing differences between different dialects might be interesting and useful. But if the point of the class is to teach SQL, such digressions would not only be a burden on the professor but on the students. Students would have to be sorting through, what dialect is he talking about now? Does that apply to me or is that one I'm not using? The level of complexity would be greatly multiplied.
And how would he grade assignments? Is he supposed to have all these different dialects installed on his computer? And keep track of which student is using which dialect and be sure to test with the right one?
What if someone said, "I don't want to use SQL at all. I want to use Mongo"?
What if someone said, "I don't want to learn about databases. I want to learn about writing flat files"?
What if someone said, "I don't want to learn about databases. I want to learn about the French Revolution"?
Where would this end?

Unwise to drag textbooks into this.

@Joshua Not sure what your point is. The bit about textbooks was an example.

It's a fight with more at stake than the language variant one; you could easily lose the textbook one due to the weight of the other side and still win this one.

In general yes, but perhaps not with artifically "pushing" a specific commercial tool
I agree with the multiple answers as a matter of principle - teachers can and do choose how to teach their classes, and have much leeway regarding choice of tools for the students to use, as well as form and syntax for homework submission.
That being said, if the teacher of a course forces the use of a tool and/or dialect which:

Is only provided by a single commercial entity
(perhaps) Requires the students to expend financial resources on gaining access to this tool
(perhaps) Requires the university expend financial resources on gaining access to this tool
Has more commonly-used, freely-available and well-regarded alternatives
and the teacher refuses not provide a (minimally credible) rationale for exclusively using the tool

... then this may be ethically problematic. It is effectively a form of coercive commercial advertising on the teacher's part - if not artificial revenue-generation for the provider of such a tool - and may be contrary to the academic institute's regulations, or possibly to state laws.
Now, I'll grant that this is tricky business, since we're talking about regulating academic freedom with somewhat extrinsic considerations, so it would have to be rather egregious. I doubt that saying "Use the Oracle SQL variant" meets the above criteria, since for the purposes of your course it's probably effectively the same thing as ISO SQL; or can be trivially adapted from other SQL variants; and apparently there's an "Express version" which you can use for free. But in the kinds of cases I described above it is not as cut-and-dry as other responders to your question suggest.

Maybe. Oracle is available legaly for free (the "Express" edition); nobody needs to expend financial resources. It is debatable whether there are "more commonly-used" or "more well-regarded" alternatives, this surely depends on whom you talk to. But it is certainly not a seldomly used and not a questionable tool; and it is common enough in the industry that it is highly likely that a student will have quite a benefit from having it on their CV. It's also hardly a case of unethical advertisement or something like that.

@AnoE: See edit.

I upvoted this. Just once, I want to see the instructors academic freedom overthrown by the students of the class. I am sure such a power would be abused heavily, but when the instructor insists on teaching largely obsolete technology it is time for the instructor to go.

@Joshua: I would not want to see academic freedom overturned, and certainly not by students. Docents do have some ethical obligations, even while enjoying the freedom to teach as they see fit. (Actually, they don't effectively enjoy it anyways.)

@einpoklum: I take it you never discovered a major-required class you were in was utterly worthless half way through.

@Joshua: To be honest - no, I have not. Remember, though, that universities can assign major-required classes to teachers who teach them more seriously, without forcing a specific approach on individual teachers.

I recall Oracle being free to use for learning at least 10 years ago, long before most systems were.

@Ian: PostgreSQL, MySQL, MonetDB and others were always free to use, and libre - including 10 years ago.

@einpoklum, If I recall correctly most of these were even more of a pain then Oracle to run on windows.   They were also very new at the time.

@Ian: Windows is another commercial piece of software, and that one definitely isn't available for free. Also, this discussion is veering off topic...

First time I've ever heard anyone call it ISO SQL..

@CaiusJard: SQL is really just ISO/IEC 9075.

Most universities in the UK communicate information about their courses and modules by so-called course and module descriptors. They are often a part of your course handbook or provided in the module information pack.
Read the module descriptor and take a look on Learning Objectives for the module. They describe the skills and knowledge which you will obtain after a successful completion of this module. Your lecturer is here to help you acquire those skills.

If the LOs say you will learn a language/syntax, then this is what you need to learn. Presumably a choice of language is well justified since it made it into a formal document (which is a part of your learning contract).
If LOs say you will learn how to manage a database, then you have to manage a database using any suitable language. If the lecturer wants to impose a restriction, she/he should justify it using objective reasons, e.g. this program is outdated, this is not suitable for the task, this is proprietary and University does not have a license for it, etc. Forcing individual preferences on students without academic need for it is not cool and only shows that the lecturer has limited skill set and can't be bothered to learn more. This is not a good example to follow.

I think that admitting you have a limited skill set is a very good example to follow. Anyone who doesn't admit their limitations is not to be trusted.

This overlooks the overhead (possible automatic test harness, etc.) in assessing/grading a multitiplicity of language submissions.

@MichaelKay It is cool to admit your limited skills, but not cool to make your limitation someone else's problem, in this case your students' problem. Lecturers' task is to help students develop their skills, not restrict them.

@DanielR.Collins Grading is lecturer's job. It's not fair to simplify your job at expense of reducing your students' experience.

@DmitrySavostyanov: We'll have to disagree. Being efficient with your time so you can help more people more deeply is wise.

@DanielR.Collins We surely have to disagree. Being "efficient" to provide worse support to more people would never be my goal.

Per my last comment, I'm interested in helping more people more deeply, e.g., with better-prepared class presentations, more time for personal meetings and advising, deeper feedback on concepts for assignments, etc. It seems odd that you'd instead prioritize budgeting time to support students not having to learn a new language.

@DanielR.Collins I want to focus on helping students to learn what they want to learn and what the module descriptor promises they will learn. If they came to learn data analytics, for example, they can choose a language for their assignment - language is just a tool to help students learn the subject. Restricting their choice of language only reduces their experience. And I am happy to learn a new one, if I have to do it for marking purposed. How can I encourage students to learn if I don't want to learn myself?

It's not about the instructor not knowing a language, it's about constructing a single test harness to help automate grading and save time for other purposes. Refer to my first comment; now we're just arguing circles.

@DanielR.Collins So, you argue for automatic grading and call it "deeper feedback" and "helping more people"?  There are definitely some circles here.

I did not say, nor use, automatic grading. You're being intentionally obtuse.

@DanielR.Collins You said "to help automate grading" and "automatic test harness". My first language is not English, and I apologise if I misunderstood you, but what is the key difference between your approach and automatic grading?

For what its worth, with Oracle and PL/SQL you will get not only the core relational database and SQL experience, but also some hands-on experience with some aspects of a commercial database and its extensions to the standard. This will enable you to pick up just about any of the other standards-based RDBMS systems on your own and it will instantly feel familiar. You may not have another good opportunity to learn a commercial database prior to getting into the job market, and having Oracle PL/SQL on there will look great and possibly open some employment opportunities that would not normally be available to new graduates.
I'll add my own experiences to the pile for contrast. The lectures for my Introduction to Object Oriented Programming class in the mid-90's was taught in Fortran 77 (a non-OO language) because the (tenured) professor didn't know C++ and this was his last semester before retirement. He lectured using his notes (written on punch cards) from an earlier Data Structures class (based on an unrelated textbook), while the programming labs were taught in C++ by graduate students who actually knew what they were doing (thank you, Hamid). The exams were blue-book (written essay) and required answers to Object-Oriented programming questions drawn from the C++ lab assignments...written in Pascal - a language that none of the students in the class had been taught but were presumed to already know. Every student received a 2.5 and the professor retired. I learned OO programming from a graduate student during a two hour-per-week lab assignment, while his attention was split among 15 other students.
Another example from the following semester (more relevant to your situation). My Database class was taught without databases at all. There was no database, no SQL. The programming labs were entirely in C and involved assignments like implementing core data structures to support relational tables in memory, creating different indexes based on binary trees, etc., and writing queries using table scans and then the indexes, then evaluating their performance using big-O notation and refining the indexing methods. Even though I did well in the class, it provided no practical experience with any DBMS or query language. It was a Computer Science course in the purest form. In retrospect, the concepts stuck with me and it did help me to understand how to debug queries for performance.
In summary, at the university level you need to choose your battles wisely, and I hope this convinces you that your current course is not so bad. It is up to you to find ways to get the most long-term value out of your courses.

I agree with all the other answers: of course the lecturer can do this, and is likely to be using Oracle to grade your work.
I write to add: I think you should avoid Oracle extensions to or deviations from SQL, unless they greatly simplify the answer into something elegant. The specific difference I have in mind is Use standard SQL JOIN syntax, not Oracle's old WHERE based syntax. The former can express relations that the latter can not, the latter is not accepted by other RDBMSes, and I can't believe your lecturer would be using a version of Oracle so old it didn't also take the SQL standard syntax.

all_licenses: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


text:
What effect does a journal having multiple ISSN codes have on reporting citations?
Recently, I submitted a paper into a general whose name also appears on the SCIE list of web of science. My paper got accepted. The journal actually has two ISSN numbers (one for print and the other for online). Only one ISSN (print) appears on the web of science list. I want to know what does it mean? Will my paper be included in print version or not? (The journal did not ask about this choice at any stage of the whole process.) If it is not included in the print version then can I write the impact factor of the journal next to the title of my article on my personal website?
I will be very thankful for clarifying my confusion.

I am confused. According to ISSN web page: An ISSN (International Standard Serial Number) identifies all continuing resources, irrespective of their medium (print or electronic)

What are the ISSNs you are looking at? This would make it easier for others to troubleshoot/answer.

I have encountered with this problem. I think it’s a kind of fraud. If a journal have two ISSN, both of them must be same and not different!!!!!!

From what I can understand, currently active journals that are published both online and in print will have both a p-ISSN and an e-ISSN. This means that your paper (which will be published in both channels) will be attached to both ISSNs. See Wikipedia:

When a serial with the same content is published in more than one media type, a different ISSN is assigned to each media type. For example, many serials are published both in print and electronic media. The ISSN system refers to these types as print ISSN (p-ISSN) and electronic ISSN (e-ISSN), respectively.[citation needed] Conversely, as defined in ISO 3297:2007, every serial in the ISSN system is also assigned a linking ISSN (ISSN-L), typically the same as the ISSN assigned to the serial in its first published medium, which links together all ISSNs assigned to the serial in every medium.[4]

ISSNs of different types for the same journal are connected through ISSN linking (ISSN-L). A file of ISSN links can also be downloaded from the ISSN website, to check whether specific ISSNs are really linked. You can also read about ISSN linking at wikipedia: Wikipedia: International Standard Serial Number#Linking_ISSN.

Do you happen to know any journal which has both p-ISSN and e-ISSN assigned to it?

I think most current scientific journals have both. See e.g. Journal of Ecology, which shows "Online ISSN: 1365-2745" (i.e. e-ISSN) on their website, while the ISSN shown at Clarivate Journal Citations Reports (previously Thomson-Reuters) is  0022-0477, which is the print-ISSN.

Do you think there will be journals that publish your paper only in the online version (which is not indexed in known databases like scopus..) and leave the print version only for high end recommended papers?

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/


text:
How can a prospective PhD student know beforehand which universities have the most reputable teams/supervisors?
I am interested in astronomy/astrophysics and cosmology. It is my understanding that choosing the best supervisor/team to work with for the PHD is more crucial than choosing the best (and most reputable) university.
If the above statement is correct. How can one identify the desired supervisors/teams? Probably the best way to to this is to read papers. The researchers that publish the most papers are clearly active in their field. But how can one, without even knowing these researchers find a way to locate the researchers who seem to be making good contributions to their field?
To put it in a simpler manner, does a website like arxiv.org and others have a way to identify the most active researchers (or teams that work on a specific problem)?

A good supervisor is not necessarily prolific, and prolific researchers are not necessarily good supervisors... however, if it's cosmology you're after, let me put in a good word for my own department (ICG Portsmouth).

Why do you want the most repukable? You may risk getting famous and then they will never let you be on your own.

@mathreadler that is a risk one should be willing to take at the prospect of working in the frontiers of scientific knowledge

@user134132523 Hahahaha, yeah.

Actually call someone there (or email etc) and ask them.  A call (if possible) will allow more honest, frank answers as of course an email is easy to forward.

Most active does not mean most impactful or most influential.  Reading papers on your own, you will have a hard time distinguishing good papers from mediocre work.  Your best bet is to get recommendations from people who already work in that field.  Ask professors at your own university for recommendations or opinions.
If you have a specific problem you are interested in working on, then it does make sense to search for papers on that problem and do your own reading.  But you will still want to get advice from one or more people at your own institution who work in that area (or close to it).

Other answers suggested asking professors or asking students.  I suggest you:

Figure out what you want to do after your PhD, as specifically as possible
Find people who are doing that
Ask them who would be a good supervisor for your PhD

This strategy will get you the answer most customised to your needs.
Several of the answers suggest using Google Scholar or similar.  Google Scholar will tell you which supervisors are producing nothing, but it will not tell you the difference between supervisors who publish a small amount of good work and those who do a lot of poor work but effectively manipulate the citation statistics.

Further to this answer, let me suggest that what you really want is a department and supervisor that will be of most help in developing your PhD and subsequent career.  So try understanding that directly by looking into people who have studied at the relevant departments could be interesting. You could ask staff and students how PhDs have gone: how many completed at all or over how long. Getting current and former students feedback on supervisors would be great, if they are willing to share.

Just to add to the other answers, once you have found a shortlist of possible supervisors you can ask their current/recent students for their opinion. Most academic departments will have contact details for current postgraduate students.
I myself am a PhD student and have happily answered a few queries about my supervisor.

Google Scholar and Research Gate are two general resources for finding a researcher's impact in their field. ArXiV also can provide some general information on who is recently publishing in the field. Individual/group websites can also provide some input as to the overall reputability of the professors in question.
I would consider compiling a small collection of universities you are most interested in on the whole, then researching the professors at those institutions. While university reputation is not an exact indicator of individual output and impact, the two topics are on the whole correlated. It is relatively likely that a well regarded university is going to have well regarded researchers as well.

Check their Google Scholar pages and see how many citations they receive and whether they have recent works etc. Check whether they participate in top tier conferences and published in top tier journals. Check their former students and try to find what they are doing / where they are working now.

Firstly, a good idea is to roughly estimate reputation through citation metrics. Determine 3-5 most reputable journals in your field of interest (typically that would include Nature & PhysRev Letters for physics). To find these journals, go through some major lab publication lists to compile the names, and then look up citation metrics for them. Look through your shortlist of labs & professors and you'll see some of them would get 2-3 papers in these per year, while others maybe can get there once per several years. Also you can directly look up citation metrics for professors on Google Scholar or similar services.
Now, while reputation and citation metrics are important, it tells little about their ability to handle PhD students. To get an idea about that, for each lab compile a list of recently graduated students and try to find where they are now. Are they at major world-known universities/labs? What are their positions - are they jumping from one postdoc to another for years, or do they have a more respectable position after just one or two postdocs? What did they publish while studying, are their papers in reputable journals, are they well-cited? And not the least important, how many years did it take them to graduate? If an average student spends 6+ years on their PhD, it's probably a red flag.
When researching publications, discard any conference & poster presentations - students can get plenty of those with very little effort. Look only for legitimate publications, and ensure they are well-cited (ignoring self-citations).
Typically, reputation of labs is well-correlated with university reputation. Of course, there are some dying labs in respected unis, there are some great labs in otherwise mediocre ones. But start from top universities. After reading through publications and citations for a few hours you'll pick up on familiar names and get the general idea about the major influencers.
Good luck!


text:
Permissions and copyright for a figure from an unavailable source
I would like to use, in a journal manuscript, a figure for which the original source (a website) no longer exists. What can I do?
I found the figure in a Ph.D thesis, the author of the thesis is however not the copyright holder of the figure. But the website of the figure's reference no longer exists, and is also not saved in the internet wayback-machine.
I have a copy of the figure, but i do not see any possibilities on how to ask permission to use the figure.
Any suggestions on how to proceed?
I don't want to make this question too case specific, because I think it is a problem that could occur often, in different countries and settings. But of course I would like a practical resolution: the specific source indicated in the Ph.D. thesis is A2Engineering.com retrieved march 2010.
I already looked a lot on google, also using reverse image search, and I could not track the copyright holder, or their nationality or country of residence. The difficulty is that there are many companies with similar names, but their field of interest does not seem to match with content of the figure.
Is there any chance that i could still use it, based on the idea of "fair use"?

The question as it stands can not be answered untill you add the following information: Who had the copyright and what country were they legally based in.

@BasJansen: If the website where the figure was originally taken from is gone (and with it, probably all traces that lead to the original author), how is the OP supposed to know who was the original copyright holder and what country they were from? As it stands, I read the question as: "I want to use possibly copyrighted resource X, but the copyright holder of X cannot be determined. How to proceed?"

How important is this figure? Are you able to reproduce it or do you really truly need the original figure?

@Hjan For future reference, a snapshot by the Wayback Machine (https://web.archive.org/) may be triggered by the users, too. Some addresses are working better than others, e.g. https://web.archive.org/web/20000815222443/http://www.columbia.edu:80/cu/gsas/

I will survive without using the picture, but since the situation can occur to other people to. I was hoping that some one could give some more insight on the use of the "fair use" clause of copyright, for such a situation. (even in different countries). Some times when an old book or conference paper is not accessible, people also use secondary sources in their references. Maybe something similar procedure  could apply to figures? I am not worried about getting sued for using the figure. i just wanted to know what proper "research" ethics are in such a case.

@O.R.Mapper The name of the website could already give a good indication of which company it was that likely had the copyright, from there on we could either track if the original company was bought (including copyrights) or if it went defunct. In the case the company went defunct we'd have to look at the copyright law of said country.

@BasJansen: You mean something like www.weprovidefreewebspace.com/users/~smith598?

@O.R.Mapper I said could, and the name that the OP edited into the question gives some indication already as there currently are two companies using that name (UK, India) but both seem to be to recent to be the potential copyright holder. Obviously, if the name was something like you mentioned then it wouldn't work (hence again, the could).

What type of figure is it? Is it a chart, graph or table that merely conveys data (not copyrightable in the US), or something with more creative intervention?

I'd consider to redraw the figure and/or reference the thesis, which is traceable.

I'd also suggest to redraw the figure. You should still mention the original source, but this might avoid many problems.

Did you reach out to the Ph.D. thesis author? Even if the author is not the copyright holder, presumably he or she sought out legal permissions at the time, and may still have a record of an email address or a name to reach out to. If you did reach out already, the question should be amended to make this clearer.

We have a serious problem, worldwide, with copyright now. To protect yourself and your reputation, I think you are better to assume that you cannot use the figure or a recreation of it unless and until you can determine positively that you can. Copyright differs greatly by country now. Unfortunately many works have been abandoned by their authors but copyright law generally makes no provision for that. Fair use and "use for research" differ greatly by place.
My suggestion is to give a reference to the original, even if it cannot be found and to create your own figure separately from this one, using, if possible, different data. Don't try to make it similar. Don't try to disguise the original. Make a new figure that illustrates the point you are trying to make in your own work.
I would do something like presenting my own figure illustrating the point I'm trying to make and the note that author X made a similar point at Reference Y, including illustrative figures (without reproducing them). Your readers are informed. The previous rights holder is respected. You have done the best you can do given (possibly unreasonable) restrictions on sharing.
Whether or not you would be "caught" or "challenged" in reproducing something without permission, your own reputation can suffer. It is better to be conservative here.
But it is also better to use whatever position you have to work toward more sensible copyright rules that deal with abandoned works and with scholarly research without having the original rights-holder suffer.

As many commenters have pointed out, I would recreate the figure and state something to the effect of "similar to source X" and include a citation.
If you've got through all reasonable steps to get permission, this should be adequate - you're providing proper attribution and not claiming it as your original material. While this clearly meets the standard in my country, I acknowledge it may be different from location to location.

spirit of which law? Half the problem is that this varies considerably from country to country.

@FábioDias, thank you for that.  I've make adjustments to reflect your point.


text:
What good is engineering research with no practical relevance?
I am a Ph.D. student currently doing research at a top engineering school in North America.
I am becoming more and more jaded at the fact that a sizable portion of the research conducted at my university as well as publications to engineering conferences seem to have very limited practical relevance, and with no attempts to address implementation concerns. Many of these papers seem to be published just for the sake of it.

One glaring example is power engineering. The methodologies
proposed by recent graduates from power engineering are so extremely far-fetched from practical implementation, it raises the question as to why any such
research should be continued.
Power is a very safety critical field: people can die after going for too long without power (case in point), and the industry itself is highly government regulated. The algorithms that have been proposed from my
research department as well as many like it completely ignore things
like safety guarantees. Furthermore, it is highly unlikely that
government employees in the power industry would rely on some biology
motivated or learning based algorithm to arrange the power supply to millions of actual
people. There are decades old well-regulated power markets for that!
But power is just one example out of many. I have read many papers on
signal processing and control theory. Most of the papers are completely
math and proof based; their proposed methods are so mathematical, with extremely limited robustness or safety guarantees, etc. These researchers are more concerned with epsilons and deltas than how their proposed methods can be realistically
implemented in people's cars or mobile phones.
An "implementation"
nowadays is just a MATLAB simulation, a few equations, and a graph. Even during undergraduate
engineering training, we have seen how difficult it is to go from
simulation to actual software/hardware that people can use. I can easily show you highly technical papers from these fields published by people who do not even care about the readability of their notation, let alone practical implementation.
So it is a legitimate question as to why anyone would ever use these
highly-theoretical, and assumption laden research results. It is
unclear what "the small-gain signal must belong to a Hilbert space on
the extended half-line" actually means in real life cache design. Furthermore, many
papers are completely without any mention of practical implementation of
the algorithms, so it is completely unknown if anyone would actually
be able to use these research results.

Engineering research is ultimately used to create new technologies that promise to improve the lives of people. However, it is unknown to me at this point how a "bat-echolocation based meta-heuristic algorithm for nuclear generator dispatch" could benefit anyone.
So my question boils down to how we as researchers should attempt to bridge the gap between the highly mathematical, highly theoretical modern engineering research and the practical implementation of research results. What good is engineering research with no practical relevance?

Comments are not for extended discussion; this conversation has been moved to chat.

Pseudo-applied science is often a waste of resources. This does not mean that fundamental or basis research is pointless, but there is a difference: Having an abstract model for something "out of reach" can be fruitful - one can study it, and add more obstructions in the future.
But there are seemingly applied models which draw an unnecessarily amount of attention. They are either too simplified and already well-understood so that you should move on to more realistic models or they make totally unrealistic assumptions which allow you to apply a method that would otherwise be infeasible. In both cases the models "survive" in the community because the number of scientists is large enough to form a "peer review group" so that papers get published.
Summarized: Fundamental research is helpful, but beware of constructing "applied models" that do not have applications.

Voted up because this is a point worth raising, but I am not sure how "often" is pseudo-applied science a waste of resources. I think that painting research as more applied that it realistically is at the point of writing is hurting science, but it will only stop when we will put less emphasis on applicability when judging research.

More than once I saw a model that was really strange, and after all the argueing why it is "useful", one saw that it was exactly tailored for the solution method that  appears later in the paper. If you try to solve the Traveling Salesman Problem as "very abstract version" of transportation, this is fine. If you impose some conditions, argue why they fit "practise" and then reveal that they are needed for specific solution method, you are doing pseudo-applied science.

I think this answer really gets to the crux of my question. I am not complaining about engineering research that are published without immediate application, i.e., fundamental research. It is that a significant subset of engineering research purports to solve a host of problems, yet there is very little evidence that it can actually do that beyond what the paper shows. For example, learning algorithms used in mobile communication. It is very difficult to see how humans and their mobile phones conveniently fit into a learning framework, whose  behavior can be modelled as learning algorithms.

As an engineer who spends most of my time developing numerical methods (and not publishing the good ones, since I'm in industry and we prefer kicking sand in our competitors' faces to telling the world how smart we are!)  "pseudo-applied science" makes me think of economics or psychology more than engineering research ;)

Fabian, I agree with many of your points. However, I think that your answer would be improved if you could define in the answer what you mean by pseudo-applied science.

@DetectiveMooch You would also be surprised by what can go into the radio of a modern smartphone. In the near future the phones could easily run learning algorithms to optimize bandwidth, without most humans being aware of it.

The short answer to your question is that you are vastly overestimating your, and other engineer's, ability to judge what techniques will ever have practical relevance.
I think it was Michael Stonebraker, a Turing award winning computer scientist with no lack of practical impact, who said that the sweet spot for academic applied research are techniques that are about 10 years away from being widely implementable. If you limit yourself to things that you can already do today, you will fail to propose the kind of radically new developments that should, at least in theory, distinguish academic research from other drivers of innovation, such as startup companies or industrial R&D. Incidentally, if the lack of impact your work has right now is distressing you, you should ponder the question whether you would not achieve higher job satisfaction in a startup or industrial lab.
I find your example of self-learning power grids particularly unconvincing. If we rewind time a few years and relate your arguments to research into automated driving, I am sure you will find plenty of people who found this research to be a waste of time. Driving surely is a safety critical field, and automotive is highly regulated. Algorithms for automated driving assistance completely failed to, and to some extent still fail to, address the practical concerns of many stakeholders as well as governmental safety guarantees. And yet here we are. I am not sure if the same will happen to power grids, but it is absolutely plausible that it will.
You may also be interested in reading into TRLs (technology readiness levels), as used for instance by the European Union's framework programmes as well as NASA. Image

Technology Readiness Levels

TRL 0: Idea. Unproven concept, no testing has been performed.
TRL 1: Basic research. Principles postulated and observed but no experimental proof available.
TRL 2: Technology formulation. Concept and application have been formulated.
TRL 3: Applied research. First laboratory tests completed; proof of concept.
TRL 4: Small scale prototype built in a laboratory environment ("ugly" prototype).
TRL 5: Large scale prototype tested in intended environment.
TRL 6: Prototype system tested in intended environment close to expected performance.
TRL 7: Demonstration system operating in operational environment at pre-commercial scale.
TRL 8: First of a kind commercial system. Manufacturing issues solved.
TRL 9: Full commercial application, technology available for consumers.

The basic concept here is that academic research is usually best suited to bring ideas from TRL 0 or 1 to 3 or 4. The "Matlab implementations" you complain about may very well just be the laboratory tests that are meant on TRL 3. This is very much in line with the position in the grander scheme of the progress of technology that many large organizations envision for academic research labs.

@Magicsowon That reminds me of some ESA calls, which may not fund science but which may fund a demonstration that their data can be used for science.

I am not saying that self-learning power grids is not going to happen. I am sure that it can already be done on small scale (That being said, I've seen larger-scale "learning/neural..." projects that were sent to system operators and promptly rejected). I think research should focus on bringing self-learning power grid into reality, rather than merely talking about it on paper. That is, more papers published should focus on the real-world, practical applications of their proposed methodology. And this is a call for a shift in research culture, rather than finger pointing at some researchers.

@DetectiveMooch Are you sure you should build self-learning power grids now? How well do they work in theory? Just from what you say, I'd bet not well enough; if some problems are visible in the theory, those problems can be addressed in the theory first, before the experimentalists get involved.

@Blaisorblade You are nitpicking on a particular example (out of thousands) that does in fact show some promise of being realized. There are thousands of engineering disciplines out there pumping out research that are rooted based on unrealistic assumptions, and with very limited applications. This question is about the value of those research.

I'm nitpicking a "glaring example", in your words. I and others have answered elsewhere on unrealistic assumptions.

@DetectiveMooch As I said, bringing things that already exist into practice is largely not the job of academic research. For this, you don't need papers and PhD students - you need a business case, VC funding, and good salesmen.

you are vastly overestimating your, and other engineer's, ability to judge what techniques will ever have practical relevance Either this, or the researchers overestimate their research, or they are just lying.

@user12956 Aside from a few media luminaries, most researchers don't pretend like they know what the future will be like. By nature, most real research is of the "throw things to the wall and see what sticks" variety. As people here are prone to say: if you already know that it will work, it's not research. I would argue it's the same for impact.

The critical point, imho, and in respect to many comments on many answers, is that none of us are capable of knowing what will be relevant ten years from now. Any judgement of future relevance is naive, and logical extensions of them fruit from the poison tree. Quite well-said @xLeitix.

Things with "no practical relevance" are not necessarily useless. They may just be "waiting for their time."
For instance, the phenomenon of ionic liquids was first discovered in the early 1900's, but they didn't catch on economically or industrially until the early 2000's when they were "rediscovered" and brought to prominence as "green solvents."
So it's probably unfair to say something has no possible practical relevance. It just might not be obvious yet where they could be used in the future.
Another point to consider is the possibility that someone is engineering but not really doing what is considered "engineering." This may have been a hiring decision, or someone finding a home for where they teach rather than trying to find the right home for their research. (That is the situation for me: I am an engineer by training, but my research could just as easily fit into a chemistry or materials science department.)

Good point. It was only after the death of Mendel that people realized the importance of his work. Also, the concept of negative numbers was seen as very absurd for thousands of years. Same with the concept of fractions and decimals. I wonder if Aryabhatta had considered the importance of the invention of the concept of zero. These were all useless concepts at some point of time. But today...

I agree that things with "no practical relevance" may be just waiting for their time. I can definitely say this is the case in fields like material science, certainly we have no clue what the possibilities are for the vast majority of newly created meta-materials. However, in engineer fields that are more theoretical, there are often assumptions of infinite amount of times, or limitless amount of information; these things can never come to realization. In some paper you see that, assume "every person in an area has a information receiving device that tells them"...but how can that be?

@DetectiveMooch ever heard of a cellphone? Modern smartphones are exactly "an information recieving device that tells them...", and they're incredibly widespread now in the west

@Leliel First of all, we are talking about every user in an area the size of three or more states combined together. If I were to finish that line, it would say that every user is receiving a  continuous-stream of data (high-dimensional, by the way) that captures the state of the power system. Each user also has perfect access to the state of the house such as temperature, energy usage, etc. This algorithm then allows users to adaptive shift load off of the grid.

@DetectiveMooch that is not really fundamentally any different or harder. It is expensive to implement, but not even close to actually technically hard. It just takes the power companies spending the money to give all their customers a computer with broadband (on the power companies money) as the meter.

@DetectiveMooch Unrealistic assumptions do not make a result useless — it can't be applied directly, but that's not the goal. Your argument shows that the theory of ideal gases is useless because it's built on unrealistic assumptions. My high school physics suggests that the theory of ideal gases is a stepping stone to more realistic theories. In that case, it's also a decent first approximation of practical gases.

As I recall, the laser was invented without much if any idea what practical use it might have. I'm glad the OP wasn't around criticizing work in that lab.

Guys please, attack the idea and not the personality of the OP. And especially think twice before commenting when you are not working on engineering research.

@DetectiveMooch As someone in industry, when I've seen real jumps forward it has been ideas from two dissimilar fields being merged together.  So a paper in remote sensing that makes a set of assumptions that are wildly unrealistic for their proposed application might be found by an agronomist who comes up with an idea for using it to efficiently target fertilizer distribution.  Even wildly impractical ideas can cross pollinate across domains beautifully.

If the liquid was "rediscovered" what was the point of the original research?

Research which shows new methods does not have to demonstrate the practicality of the new methods to be useful. An example from something that can be very applied is research in numerical solvers for ODEs. The vast majority of methods which have been created are not used in production-quality ODE solvers. They just aren't efficient. But having a comprehensive literature to pull from can be really helping when trying to learn about the possibilities. Someone outlining a method which isn't very efficient might've contributed new ideas for how to adapt to a certain case that in the future someone else can use to create something that is actually practical. And having a publication which implicitly highlights "look, this thing really only works in special cases because of X" helps someone else in the future when they have that idea (it's much quicker and easier to read a paper and go "okay, that doesn't work as well as I'd hoped" instead of building it yourself).
This also relates back to publication bias. Publishing that something doesn't work is just as valuable as publishing that something does work. Of course, modern publication practices require "significance" so generally researcher have to be sly about how they write the abstract ("we find that in conditions X, Y, Z that this method may be more efficient than current standard choices"), but it's pretty clear from the paper what it actually means in practical terms.
In the end there's a wave of information that moves forward and almost accidentally stumbles upon ideas which work, and these stick and become used in industry. Meanwhile, research continues onward to see what else it can find.

Your example of numerical ODE solvers can be extended a bit. Good numerical ODE solvers exploit smoothness of the solution, at least at most points. Stochastic ODEs don't have this smoothness, so adapting the good methods for ODEs to SODEs is pointless--and in fact can create serious issues with things like spurious cross-correlations. The good SODE methods are actually adaptations of the very simple ODE methods. Having these already in the literature at the time that SODE methods were being developed was surely useful.

Well, these days most of the good methods for SODEs are not adaptations of simple ODE methods. But the theory for there development draws a lot from ODE theory, and the heuristics like leading truncation error coefficient analysis has been paralleled in SODE literature with great success. These theories were created to answer questions about why some proposed methods ended up not efficient, so those methods have helped many better methods be created via the contrast. You can see the same development in L-B-S stability of implicit RK methods.

Another good extension is solving large eigenproblems. Today's "goto" methods are often based on math discovered and published by Arnoldi and Lanczos in the 1950s. In the 1960s this was generally regarded as very interesting theory, but unusable in practice because of the unpredictable accumulation of catastrophic rounding errors.  If took another 20 years until cheap and simple methods of zapping those rounding errors were discovered - and probably another 10 years before "non-experts" had access to the algorithms for solving real world practical problems. That's 40 years to reach "level 9!"

I think the other answers look at the question in one way, but I'm going to answer it from a more 'human' perspective.
Because it's interesting.
Most people do research because what they're researching interests them.  They're not trying to make millions (quite the opposite usually - they're often not well paid); they're not trying to change the world (even if they do so later); they're just really interested in if XYZ is possible.
The fact that sometimes you get something really ground breaking from research that changes how the world works means that companies are willing to invest in the research; but on a personal level, if you're not interested in doing the research, then there's not much point in doing it.

Are researchers really not well paid? My sister is currently looking for a postdoc research job in mathematics and apparently most of them offer quite some money. Significantly more than I can get in digital hardware development (generally a well paid field) with my master’s degree.

Salaries of researchers vary wildly by field, country, and stage in their career (phd, postdoc, permanent), and also by whether they work for a private or public employer. Some researchers have a great salary, others not so much. However, it is a safe assumptions that a vast majority of researchers do not get into research out of pecuniary interest.

Would love to know what part of this the downvoters found not helpful.

A lot of research is currently impractical and theoretical.  But that's why lots of it is university research rather than commercial R&D.  And sometimes it becomes very useful, eventually.
When semiconductors were discovered back in 1821 no one in their wildest dreams would have realised what an impact they would have.
And Lasers.  A great idea to shine a coherent single wavelength light at something: but why would you bother? And they required expensive materials which ruled out every day use.  Then semiconductor lasers were discovered. And then fibre optics, which need a coherent single wavelength of light to make a data signal go far. So now all the internet runs over fibre-optic cables fed by tiny cheap lasers, totally revolutionising everything, due to pure research which sounded very unlikely to yield anything useful about 70 years before.

Why does anybody study drugs on animals? We don't care about healing lab rats. But we hope that helps us study drugs on humans.
Similarly, the goal of solving problems in oversimplified settings (lab rats) is often to help solving those problems in more realistic settings.
Lots of research is not targeted at direct application, but at other researchers. This is a feature, not a bug, because researchers need to build on something. The path from a paper to its application does not need to take a single step. Also, not all of the theoretical papers need be applied. Some of those theoretical results aren't good enough to be applied, and you can tell from the paper itself—more theoretical work is needed before they're worth applying.
Sometimes, even when you know where you want to get, but you have no idea of the path, going in somewhat random directions at the beginning is more effective than targeting the destination directly. (Saw a keynote about a formal study of this in optimization problem).
In a neuroscience class, we discussed how models help understanding the brain. A researcher taught us compellingly that the virtue of a model is not (only) in what it includes, but in what it leaves out. We can't understand a full model of the brain; but we can study oversimplified models to see how they behave, then check if what we learn applies on more realistic models. It also turned out that oversimplified models of the brain are useful as artificial neural networks.
Some of the papers you have seen start from unrealistic assumptions. Likely, that's to simplify their study, especially if it is a mathematical study.
Papers in slightly more realistic conditions come afterwards.
While I don't study engineering myself, I study computer science (programming languages), and we also have many papers which consider simplified scenarios—many of those papers are still indirectly relevant, though it can take decades before theory becomes usable in practice.
EDIT: since you ask/question relevance/applicability: I'm thinking of mathematical papers which are motivated (implicitly or explicitly) by the goal of making programs less buggy. Lots of progress relies on doing more math of extremely abstract sorts, but going from math to more math to prototypes takes few stages.

I don't understand why you are so defensive. Certainly you have seen papers with very limited or without immediate application, or with very unrealistic assumptions, or have very limited applications. You say that in CS there are many papers that may take decades before theory becomes usable, then why not concentrate effort on making the theory useable? In CS there are definitely more room for non-application driven research, this is not the stated goal of engineering research.

On "Why not concentrate effort", oversimplifying: (1) on some theoretical papers, more theory is what's needed before making the theory usable. My field is full of people applying (arguably) stuff that is completely broken in theory (hence in practice). (2) On other theoretical papers, oversimplifying, the "mathematicians" (think) they're done, and more "practical guys" are welcome to come. At most, the mathematicians can spend more effort to talk to the practical guys, but research isn't a fully coordinated endeavor.

You never know when research with no practical relevance will suddenly become relevant.
A recent example in my field (astrophysics): The BICEP2 results that initially stated they had detected a signal attributable to rapid inflation of the early universe.  If this signal proved true, these people would likely have won a Nobel Prize, just to give an idea of how big of a deal this was.  However, others recognized that the cosmological "signal" likely could be completely attributed entirely to interstellar dust floating around in the Milky Way.  Almost overnight, astrophysicists who specialized in interstellar dust became the world-experts of cosmology.  Their research (which I will admit was important and applicable in its own right, so not an exact parallel to the OP's question) all of a sudden became incredibly relevant and important.
Another example is Einstein's theory of General Relativity.  Although an incredibly amazing theory in its own right, and extremely useful for understanding the universe, its applications to more "real life"-type situations were for many decades basically non-existent.  That is, until GPS was developed.  Without accounting for General Relativity effects in GPS measurements and calculations, GPS measurements of positions would quickly become very incorrect.  "Errors in global positions would would continue to accumulate at a rate of about 10 kilometers each day!" So decades after a theory was developed, technology finally advanced to a point where the theory became practically relevant in first the military and then our daily lives.
These examples aren't perfect parallels of the situation the OP describes, but still serve to illustrate my original point: you never know when research with no practical relevance will suddenly become relevant.

PhD research is almost by definition part of the education process, rather than part of a product development process, thus the work will appear to have little direct practical relevance.
A similar aspect can happen in industry itself where some of the applied research is directed at the latest buzword aspects, often without a clear understanding as to whether the research will bear fruit (i.e. directly, this year). More usually the work will fall away and become part of the researchers education (just like the 9 out of 10 start-up entrepreneurs).
Knowing if the work will have relevance is hard, but the learning will be useful. I have current work based on 'failed' AR work from 10 years ago..
Remember, feedback control was an invention (H.S.Black) which no-one believed, so some ideas do have their day. Boole was long dead before his (silly) ideas in logic came into fashion.
That all said, it is still wise to be sceptical and think about what will really happen to the ideas, and to what other element is missing from the fanciful studies [Most of the green energy studies will work once an effective industrial battery is developed that can be located near the point of generation, but until then....]

REASON 1 :
Research is a vast domain where expectation of solution to any particular challenge or inventing a new methodology doesn't come over night. A fully fledged commercial product available is not one scholar's Research Paper published . It's  an integrated study , Work of many scholars for decades. The best any knowledged person can do, is to present his contribution on his domain in any medium . With his/her contribution can make a remarkable resource not only in the same domain, sometimes in others too.
Sometimes a technical paper can be an inception of new revolutionary technology or ground breaking benefaction for the existing technology.
Here the contribution is important than the requirement.
REASON 2 :
Technology is a result of unexpected revolution. There shouldn't be scarcity of resources. Hence abundance of research is going on across the globe in multiple domain as the key for future or present innovation . Everyone's knowledge is precious and valuable. This knowledge cannot be shared through mouth , but only through technical papers for the present and future generations.
When any new break through happens , the resources should be ready to serve the purpose.

Research is a lengthy journey and concluding the final result is twice as long and Implementing is thrice as long

How far is it from flying a kite in a thunderstorm to building a lightning rod that can protect a barn?  Ben Franklin did both.
How far is it from Claude Shannon's paper on digital encoding of signals to the now familiar audio CD?
It seems to me that basic research has to ask questions, even when it's not clear how the answer will be of benefit.  Sometimes, the research turns out to be science research instead of engineering research, as in the kite example above.  But you get the idea.
Perhaps applied research is more down your alley than basic research.

Ben Franklin and Claude Shannon are bad examples as their work was widely recognized as highly valuable in their own time.

"Usefulness" is about perspective and perception. In my own little office cubicle maybe it is difficult to see where exactly my research results fit in. Just try and find confidence in that there exist (more or less visible) visionaries "high up" in different aspects who see where it will fit in. The bad thing about religion being so bashed and marginalized these days is that the concept of faith in things larger than us loses significance while in practice it is more relevant than ever. Maybe not in an almighty God, but in the existence of people making plans outside of ones' own perspective.

While I can appreciate faith having some relevance beyond just religious impact, this particular answer seems phrased in a way that gives undue credit to ruling out the good of the religious impact.