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Is it a minor or major revision, if only one of the reviewers asks for major?
I got a review from a reputed Elsevier journal. There're three reviewers, one decided recommend acceptation, other recommended a minor revision and the last one recommended a major revision.
Overall, is it a major or minor revision then?

My philosophy is, regardless of whether it is minor or major, it is still a revision.  Some times the hardest comments to address are those from reviewers who recommend 'minor'.

What does the e-mail say about the decision? Note that some journals don't have "minor vs. major revision", but just "revision" (which is similar to a major revision because the reviewers will definitely see the revised version again).

Major and minor revisions are not necessarily meaningfully different decisions.  https://academia.stackexchange.com/questions/55665/what-does-the-typical-workflow-of-a-journal-look-like

@AnonymousPhysicist But can be. "Minor revision" can mean that the editor will perform a final check, without sending to the authors. (Note that the question you linked to doesn't even mention minor revisions.)

Assuming the referee is right and you can't dismiss his/her points it is a major revision. What is major and minor is quite subjective. You have already all the info you need to proceed further, without giving a name to the process.

What the reviewers recommend is not important, those recommendations are aimed at the Editor, who is the one making a decision. If the Editor says it's a minor revision, then it's a minor revision. If the Editor just says "revise", then it's just a revision, without making a distinction. A good Editor will also tell you what to change, or at least point out the main reasons for their decision. If the Editor just forwards reviewer comments without adding a comment of their own, it's a bad editor. :/

You are wiser to assume "major" and act accordingly. Much less chance of a future rejection. But the specifics of the request matter.

Does it matter? If it doesn't, I find it hard to answer the question.

More I see this more I ask myself what does it really mean.

It's up to the editor to make the decision. Their job is to read the reviews and decide if the criticism is substantial enough to justify a major revision.
If the editor is lazy, they might resort to shortcuts, such as: follow the least favorable recommendation, or take the average of all recommendations. But that is a poor practice.

As far as you are concerned, your work cannot be accepted in current form but may be if you address the comments raised by the reviewers. (A good editor will make this explicit and expand on what they require and what they recommend.)
The question of "major" vs. "minor" is mostly internal (between reviewers and editor) and has two basic functions:

If it is a very selective journal, editors may decide that a major revision is unlikely to be successful within a reasonable timeframe and reject the paper at this stage rather than waste the time of everyone involved on a second round with uncertain outcome -- there are enough excellent papers submitted that can be published with only minor changes.

Related to this, major revisions are more involved and take more time than minor revisions, so the deadline for resubmission will be longer (months instead of weeks).

Whether a revised manuscript will be sent for another round of reviews is the sole decision of the handling editor; while it's virtually guaranteed for a major revision, it may or may not happen for a minor revision based on the specific comments, the responses to them, and how confident the editor feels about evaluating themselves whether the comments have been addressed adequately.
TL;DR: As an author, revision is revision; if you absolutely have to distinguish how it's been logged in the system for some reason, look at when you are asked to submit the revised version at the latest.

There's another function of the "major"/"minor" distinction which is external: a minor revision sends the signal that the paper is very likely to be accepted if all comments are addressed. For a major revision, there is a realistic chance that the paper gets rejected after the revision -- for example, because some questions about the methodology have been answered, and now it's clear that the methodology is not appropriate.

It depends on the reason for the differences.
Often, especially with multi-disciplinary work, one reviewer will not have the knowledge required to assess every aspect of the paper. The editors may pick reviewers from several different specialties so that between them, they can cover the whole work.
In that case, getting one "major revisions" and two "minor revisions" probably means that there was a serious problem, but only one reviewer had experience in the relevant field necessary to recognise that problem. Obviously the editor should then treat the outcome as "major revisions".
But if the work is within a single discipline and all the reviewers have the knowledge necessary to assess the whole work, it's possible that all of the reviewers noticed the same issue but they disagreed on how serious it was, in which case it becomes more of a subjective judgement call for the editor to decide whether Reviewer Two is being too harsh.


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Is a journal article published as a third author and a dissertation for an undergraduate student good enough for funding for master's studies as RA?
I am an undergraduate about to graduate within the next six months. I have a research article as a third author in a journal with an impact factor of 3+ related to machine learning used for Bioinformatics. I have another manuscript that may not get published, since my supervisor suddenly made me compete with a labmate for the same datasets. After showing him the results of my work, his work got published since his results were better than mine, although I had defeated previous research works on 3 out of 5 of the mentioned datasets without my name in the authorship or acknowledgement list. I felt that it was wrong of my supervisor to make me show a labmate the results of my research and make me compete with him for a publication. Correct me if I am wrong.
But now, I want to apply abroad for research assistantships for my master's in machine learning-related studies right after graduation because I'm not financially stable. My question is if my profile is good enough to start applying for funding as an RA. I have one publication as a third author in a journal and an unpublished work as a first author which I will use for undergraduate thesis dissertation. Or should I try to publish another research article before applying? How should I state my unpublished works in my CV?

IMHO, your record is impressive for an undergraduate, and as far as I can tell few people can match it when starting their Masters' work - which is when one usually first conducts publishable research.
But note that many places don't offer funded Masters' work, regardless of your achievements, or may prioritize graduates of the same institution etc.
Finally, I believe you should not completely slant your choice of institution based on funding. I realize that your financial situation is a constraint, but - diverting years of your life to working and studying some subject, and in some environment, which is not where you'd like to be is a very high price to pay.

The second paragraph warning is especially important here. In US, almost all RAs are for doctoral study.

IMO that is great!
I got into graduate school having done a thesis and being a much later author on a paper, but really it is going to depend on what funding is available/who else is applying when you apply to graduate school. Though I do not see why you would not be able to apply and hopefully have some luck.
Also if you create a section in your CV called publications you can mark some as "In progress:" Insert citation info here

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How to cite a conference paper with no pagination?
I want to cite a conference paper that is in IEEE format. Unfortunately, the paper doesn't have any page range and volume number as I've downloaded it.
Here you can see the paper that I want to cite.  I tried Zotero as well, but there was no success.
Please help me to cite this paper correctly.

What exactly is the problem? If a document has no page range, then do not specify a page range. Also, conference proceedings rarely have volume numbers (or at least, they are often not indicated); volume numbers are more commonly found on journals. Also, check out how other authors cited the paper.

Sometimes a paper you download does not have the page numbers, volume, etc. included in the electronic version. However, you might find that information from a citation/indexing database. I don't know why it is like that but at least some IEEE conferences do it. (Also, be sure you have the final version and not the accepted version posted by authors here or there.)

In these cases, the most common form is a "pp. 1-6" or "pp. 6" instead of the page range.

I am using Zotero. when it returns one page in citation it uses p. 6 and when there is a page range it uses pp. 1-6. Is that correct. Because Zotero mess up sometimes.BTW whats the volum number and its abbr of conf @o4tlulz

@OP: Actually, you can download a Bibliography entry for that particular paper on IEEE Explore that has all the available information. The page number is written as "6 pp. Vol.2-" in the entry. Since the publisher provided this information, this is the best that you get.

Thank you @DCTLib I've downloaded the bibliography entry from its page. But why it has 2 PP in the citation, Also why the names are reverse. Shouldn't it be (I. Kopcak,)
Kopcak, I.; da Silva, L.C.P.; da Costa, V.F.; Naturesa, J.S., "Transmission systems congestion management by using modal participation factors," Power Tech Conference Proceedings, 2003 IEEE Bologna , vol.2, no., pp.6 pp. Vol.2,, 23-26 June 2003

@Jamaisavenir: In the case of page numbers formatted as "6 pp. Vol.2-" (mind you, there is a separate Volume field that says "2"), "the best that you get" is, in my humble opinion, so poor that I would discard it. As for the names, it is usually acceptable to write names as either "first last" or as "last, first". Bibliography managers such as BibTeX understand both formats for their input; the formatting of the names for the final paper needs to be consistent for the bibliography and is usually imposed by the bibliography style.

@Jamaisavenir That is one funny thing with the IEEE citations. The simple text format they provide in the website does not meet the IEEE format. I am not sure about Zotero but in Endnote you can kind of define a correct IEEE format and BibTeX will do the same. Also don't worry much with the abbrev. particularly for a conference paper. You can do "in Proc. of IEEE PowerTech 2003" in this case.

I have a confession to make: some of my citations use fake page numbers.
Why do I do this?  Because sometimes there just aren't any page numbers or I cannot find the page numbers for a document with any reasonable amount of searching.  This happens especially often to me with computer science papers, where some venues just don't bother.  Online journals also often have unusual approaches to page numbering, sometimes giving a DOI-like token rather than a number.  In other cases, such as some book chapters, there are page numbers, but the versions accessible online aren't marked with them.
My preference in these cases is simply to omit the page numbers: citations are about information accessibility, and if the page numbers aren't a meaningful part of accessing that information, then I see no point in trying to fit the citation onto that particular Procrustean bed.
Occasionally, though, a journal with a particularly zealous and inflexible copyeditor will force me to add page numbers to things that don't have page numbers, and that is when I use fake page numbers, simply numbering an N-page document as pages 1-N.
Bottom line: don't sweat the page numbers, much less the volume number.

Thanks for your answer. @jakebeal  I also have another question. There is a paper that I want to cite it and write its DOI code. But there is not any DOI code for this journal. is that possible?
http://connection.ebscohost.com/c/articles/70237111/transmission-congestion-management-restructured-power-systems-by-generation-rescheduling-load-shedding-using-rule-based-opf

@Jamaisavenir Most reasonable journals these days have DOI codes.  The publisher of the article you link to, however, is on Beall's list of predatory publishers, and as such any sort of poor behavior is likely.  You should treat the article as unreviewed and of highly dubious provenance.

a journal with a particularly zealous and inflexible copyeditor will force me to add page numbers — In this situation, I recommend sending the copyeditor the DOI and challenging them to add the page numbers themselves.

@JeffE Sometimes that has worked for me.  Other times, the copyeditor has instead come back saying that clearly the citation must be invalid and should be removed, since it's not in a "proper journal."  This has actually happened to me from a prestigious venue, which I found unspeakably problematic.

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Downloading abstracts using Mendeley
I am using Mendeley for my reference management. As I am doing a Systematic Literature review, I have to filter out my search result entries based on inclusion and exclusion criteria after reading the abstract. I have exported Springer search results into Mendeley but Springer only allows CSV export of search results and does not include abstracts in it.
I want to ask: is there any way I can download abstracts of articles after importing them into Mendeley?

Please give the exact name of the database that you are trying to export to (e.g. SpringerLink) because the correct answer depends entirely on the precise database. Even better, include a URL to the database; this helps us help you.

Its SpringerLink. And ACM Digital Library also. But for now I am concerned with SpringerLink.


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What general skills must a student achieve to earn a Ph.D.?
I know that different learning and research institutions have different rules of what a student must achieve to learn their Ph.D. But what are some general or common skills students in fact have to adopt? If we have ranks like Ph.D., docent, and professor each level probably consists of some amount of knowledge or new skills the applicants have to achieve. So I am interested in knowing what those are in general for post-graduate and how they differ from Master's or docent preparation.
I could hypothesize, that the goal is to get deeper knowledge in a specific area and get in touch with certain types of research methods. But into what depth? How it differs from higher-ranking academics, etc.?

@AnonymousM Yes, I know it is not standardized and therefore the requirements are different for each commission. Nevertheless, I think that it can be subjectively averaged based on the knowledge of people in higher academics. Although it is different everywhere, degree holders must have some set of similar skills to be able to move between different institutions.

So it would be interesting to get some paper on the topic, but from my fast literature review in the Web of Science, I came to the conclusion, that the topic was not at the center of scientific research and find just DOI 10.1037/0003-066X.49.9.806, which was cited by some consequent similar studies.

I find it a bit unfortunate that the question was closed (and I thus voted to reopen). Sure, the question is broad and quite vague - after reading it first I had also gathered that it might be too broad and too vague. However, what if not the existence of several very good answers could be evidence enough that the question can be reasonably answered on this site?

Something that I have noticed among students is that they generally expect the "next" thing to be basically the same as the thing they are doing right now, only harder.  The reality is that most transitions in education represent a change in kind.  Earning a PhD is not about just studying one thing deeper, or honing a specific set of skills.  It is not just "masters work, only harder".
I think that the nature of a PhD program was very well summarized by Matt Might:

Roughly speaking, before you start your PhD work, you are building skills.  You are learning the basic language of your chosen field, and learning what has already been done.  In a PhD program, you are ostensibly conducting your first semi-independent research project.  It is in a PhD program that you learn to be an independent researcher.  The goal of PhD research is not deeper learning of what is already known, but pushing the boundaries of what is known.
Upon completing a PhD, the expectation is that you will be able to conduct research on your own.  A PhD program is about learning the skills to be an independent researcher.

Thanks for posting this. I was thinking of that same figure.

That figure is excellent! Talk about an image being worth a thousand words!

Strictly speaking, to earn a PhD, the skill set is much smaller than it is to be a successful academic. Moreover, some will become successful without some of the nominally required skills.
But, for a PhD, the normal requirement is that you demonstrate, via a dissertation, that you can do novel research that contributes to a narrow subfield of some larger field. Along the way, and contributing to that, you generally need broad knowledge of the overall field (math, history, philosophy,...) and the ability to write effectively enough that others accept the results of your research. That also implies that you know the general research process of your field.
While a somewhat narrower set is all you need for the dissertation itself, most graduate education systems insist on the set above, at least.
However, to be an effective academic, you need more in theory, though not everyone will exhibit them in practice. You need communication skills, speaking and writing. Organizational skills (more important in some fields than others). Teaching skills (more important for some careers than others). Interpersonal skills (so that others don't try to exclude you). Ethical skills (so that you don't go off the rails).
And the broader set, above, is synergistic. They contribute to one another.
Probably a few more if I think for a bit longer. But those come mostly independently of the skills required for earning a doctorate and all can be gained and enhanced over time.

I wish I could upvote this answer 100 times just for the paragraph starting with "However, to be an effective academic,...". I did not expect to meet so many academics who lack at least one of these skills, especially communication skills.

"You demonstrate that you made a meaningful contribution to the sum of total human knowledge."

The PhD thesis should include genuinely novel research on a subject. (Some exceptions can be made for simultaneous discoveries, etc.)
That implies the knowledge of the state of the art in that field to judge where the summary of prior discoveries ends and where the own work starts.
To be meaningful, the thesis also needs to conform to the professional standards of the field (regarding style, presentation, citation of prior science, etc.) and be published in a professional way.
In addition, the candidate needs to present the results in more accessible formats than the thesis, e.g. lectures, conference talks. Just writing something is not enough if the candidate cannot give suitable lectures to a group of interested undergrad students, and different ones to a group of postdocs. (Exceptions may be made to compensate for disabilities, but they must keep sight of the fact that interaction with other researchers is essential.)

The idealistic answer is that the student must present a novel discovery in their field. This means it has to be something new - you can't just redo an experiment that was already done. However it doesn't have to be significant or impactful at the PhD level. Even higher-tier programs can be pretty forgiving with "insignificant" discoveries for a dissertation. You will also have to demonstrate sufficient technical knowledge related to your discovery and the ability to communicate it clearly. These are all subjective and decided by your dissertation committee.
Also, historically, a PhD means that you are qualified to teach at the highest levels, i.e. that you may now train other PhDs yourself. These days, the job market is tough, and it is rare to get a faculty position with "just" a PhD. But in principle, the ability to "procreate" is the essence of the PhD. In the 70s and before, it was not uncommon to start a faculty position immediately after completing a PhD.
The "real" answer is that typically it is desirable that:

You publish many papers
You publish first author papers
You publish high impact papers
You are able to secure grants
You are highly employable as a postdoc in the field and ideally already have tentative job offers

I said earlier that the discovery doesn't have to be impactful. That's true, but your advisor will not want you to attempt defense if you don't have good papers out. For papers, impact matters. And while technically papers are not required to defend a PhD, your advisor's blessing is, so you see how that works.
Moreover, remember that early in the PhD there will be a proposal mechanism such as the qualifying exams. This is where you present a detailed proposal of the research you are planning to do for your PhD. Your committee might not accept your proposal if it seems unlikely to yield interesting results. So if you have already passed your quals, done the research, and the results turned out uninteresting - oh well, you "can" still defend (but assuming your advisor or committee doesn't care about lack of strong papers). But they also try to prevent that very situation at the qual stage.
There may also be some trivial institutional requirements such as must have taken X courses, must have TA'd Y times, etc. These will depend on the institution, they're not universal.

So I am interested in knowing what those are in general for post-graduate and how they differ from Master's or docent preparation.

A Master's dissertation does not require the work to be novel. However, like all degrees, a Master's also requires you to satisfy whatever requirements your advisor has. Master's advisors are generally the same professors that take on PhD students, and it's not unheard of for them to have a strong bias towards novel work. So they may insist that you work on novel things or else refuse to let you defend, whether the degree technically requires the work to be novel or not. Also, not all Master's degrees require a dissertation - some just let you complete coursework. Generally, the degree indicates advanced knowledge of a field, which may be desirable for private employers seeking highly trained specialists. A Master's degree does not normally qualify you to train other Master's students, nor indicate the ability to perform independent research, in contrast to a PhD.
I'm not really familiar with docents since they are a European thing. It also varies a lot with country and isn't much of a universality. Generally, this is an expression of a fresh PhD still not being enough to become a professor straight out of school. Since the PhD is now not enough, the grads are expected to publish some more meaty papers, so that a committee can finally certify them as suitable for professorship. Professorship isn't automatic in any country - even in places like USA with no "docent" rank, you still have to first please a department sufficiently as to get hired as a junior professor, then impress them further at tenure review to become a full professor. The things they look at are the same: Do you have many important papers, and are you getting many good grants. Just some countries give you an official title, others handle it more informally.
Technically, a docent must also demonstrate research that is independent, as opposed to how a PhD student is supervised by someone. However that's sort of a given. There's not many tenure-track post doc positions, and you usually do not get to this stage without already being some sort of faculty.
In sum, I guess you can say that procedural requirements dominate the difficulty of obtaining a PhD, MS or docentship. Therefore, many important skill requirements are implicit. Those that are required explicitly paint a very incomplete picture.

At my institute it was also common that there were no aimed research goals, instead, after x years (usually 4-7 years) you write down what you did, try to make a big picture out of it and submit it. But I have to say the success rate was not that high but however, many professors thought this is the way to go, meaning, give them a contract, wait some years and ask them what they got. Nowadays phd programs and stuff is not research in their eyes.

@Ben If Ph.D. programs are not research in their eyes, it's also their fault, because they have formed it like that.

@Ben Really? Guess you learn something new every day. I'd be pretty pissed if I spent 5 years working on something because no one told me not to, and then I fail the defense because "well you shouldn't have worked on that".

yep, this happened to some people I know. One of my best friends was or still is facing this situation..  he was one of the very best math students, got one (of the very rare) phd positions at the math institute at a rather new professor (who managed to get a permanent position meanwhile at a quite young age for a professor). After around 4-5 years this professor changed the university (to one that offered him this position) and since then he doesn't care about the phd of my friend anymore who is now pursuing his phd for 9 years, but he stopped working on it around two years ago..

p.s.: The university itself is saying "Well, the professor changed the university, we have no control over him,.." and other professors won't accept his phd as the topic doesn't touch their field. In addition, as he also had not been in a phd program and was, you could say, regularly working as a scientist (which meant he also had to hold lectures, carry out exams and stuff), there is also not any legal way to get a hold of that professor. In my eyes, he somehow abused my friend as he did really a lot of stuff alongside so the professor could dive into research and get a position, finally..

See "Advice for New Graduate Students", by Jennifer Rexford (September 27, 2010), on the Freedom to Tinker blog.


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Why don't things work with comparisons in reference letters and refereeing?
This recent question How high should I rate a good student for PhD admission? is just an example of how puzzling the problem of writing reference letters is.
One knows that there is inflation in the field, so they feel they have to adapt, but it is not clear how much inflation is appropriate and when it is too much. Negative comments are often omitted, and this gives the evaluators less data.
There is an analogous problem when refereeing papers, or serving as anonymous referees for research proposals and grants. This seems to be a flaw which is implicit in the evaluation method. Apart from this voluntary inflation bias, there is also an involuntary component: if I ask you to evaluate your research field/colleague/student is, you will unconsciously feel biased towards it/him/her.
Is this perceived as a big problem? If so, why is there no attempt to obtain more reliable results? Some possible strategies, although difficult to apply, spring to my mind:

if there are enough data points for the recommender, normalize everything and "grade on a curve".
don't ask "how good X is?"; give them two paper/applicants/projects to evaluate, and ask "which one is better, X or Y?". This makes it more difficult to find good referees, because they have to be familiar with two of the persons/projects under evaluations.
force the evaluators to provide at least 3 positive and 3 negative points, the most outstanding ones. This won't affect the inflation bias, but at least it provides some more data on the negative aspects.

I have seriously considered writing a standard addendum to enclose to my letters and reports, stating that I realize how biased and unreliable these things are, I am in a difficult position giving an absolute score on a scale that is totally unknown to me and the other evaluators, and I'd really feel more comfortable with a comparison-based evaluation system. Would this be appropriate, or raise some eyebrows on the trustworthyness of my evaluations?

I like this idea!  Editors could just ask you, for instance, to "rate this paper in comparison to two papers from our journal that you are already familiar with".  Or, to compare the paper to other papers you've reviewed for that journal.

I suggest that an academic committee, sitting in consideration of an applicant to their PhD programme, would very likely view your addendum as being an honest attempt to preserve your set of professional ethics with regard to fairly ranking the applicant.
I do not think that the academic committee would doubt your trustworthiness. However, I expect that the committee would view whatever ranking you provided on the Good/Great/Excellent/Outstanding/Genius scale in light of your action of submitting your addendum.
Therefore the committee are not going to doubt your ability to rank the applicant, but are going to doubt the veracity of your ranking on the artificial scale.
However, I would hope that any sensible academic committee is going to be aware of the problem that we are discussing here and are therefore going to take a scaled single-valued ranking of any applicant with a massive grain of salt. They must know that a single value rank is going to be useless when judging the suitability of an applicant for a PhD programme.
Thus I would hope that, with or without your addendum, a sensible academic committee would take very little notice of the single value ranking and pay much more attention to the substantive letters of recommendation.
The addition of your addendum would not, I think, damage the applicant's application before a reasonable academic committee, as it wouldn't be news to the committee that such single-valued rankings are untrustworthy. To a reasonable academic committee, your actions of submitting the addendum might prompt others to do the same, and the practice of demanding a single-value metric abandoned over time. To an academic committee that places great store in such single-value metrics, your addendum sets out your concerns and presumably instructs the committee how you the single-value ranking is to be interpreted.

I wouldn't worry too much about it.  Good schools will rely primarily on the content of your letter.  Focus on writing a helpful letter.  Include comparisons to other students who you have worked with in the past, where possible.  That will enable people to calibrate your letter.
I wouldn't don't worry too much about the numeric scores.  Good schools won't pay too much attention to the numeric scores, if you've done a good job with the body of the letter and written a detailed, helpful letter with plenty of concrete specifics.  Any school that puts more weight on the numeric score than the body of the letter is just screwing themselves by using bad decision-making processes.  You can't do anything about that; you can't fix other peoples' bad judgement.  All you can do is do the right thing when it is under your control, and leave it at that.

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How to sustain and realize a collaboration?
This might sound silly for those who know how to do it and do not see that it is a problem but for some people like me it is.
Since I started working in academia, I have had the problem of building collaborations, or to be more precise, of maintaining the collaboration and making it fruitful.
I know how to take the initiative and start the collaboration but that's it. For example, if I meet someone at a conference who is working on something exciting for me, I usually communicate with them and even schedule a call but that is the end of this collaboration (can we even call it that??). In the call, I explain what we are doing and I hear what they are doing but do not know how to move to the next step or even what is the next step.
There are people/researchers who have a magic ability to build collaborations and most importantly accomplish something out of them. Any advice?

SF author Larry Niven said (at a panel at a convention I attended) that a collaboration is a case where two authors each do 90% of the work.

In my career, I had collaborations (research and papers wrote together) with people I know or met informally, but I had these collaboration 5 or even 10 years after the first contact point and after many failures. Among my peers (PhDs when I was a Phd, Postdoc when I was postdoc), the only one that collaborated where the one bringing funds from one professor/advisor to the other, or similar agreements, nothing to do with the idillyac picture of researchers working cooperatively towards a goal.

In my career, I find out that only 10/20% (max) of these calls lead to a collaboration. It takes time, effort and luck.

I agree with @AnderBiguri . From my experience, the people who are really good at collaborations are usually very talkative. They are also strategic with who they talk to, but they definitely do not miss an opportunity to talk about their research to others. These people also tend to have a large pool of unsolved problems, but not necessarily.

If after the first call there is not at least one reasonable next step that comes to mind, then that is a signal that there may simply not be a useful collaboration possible here at this point in time. No shame in this, as @AnderBiguri notes, not all first contacts lead to a collaboration. Perhaps better to leave it at this and stay in touch - you always meet twice, and perhaps in a year or two there will be another meeting where a next step does immediately occur to everyone.

As you suspect, you haven't even started collaborating.
The next step is to figure out what you want to work on together (and whether you do indeed want to collaborate). I see three different types of choices for a project:

One of you has a nail, and the other one a hammer.

This could be the combination of one person having identified an interesting question, and the other one being an expert of a technique that might help solve it; or one person having a data set (or the means to create it) and the other person the know-how for how to analyze it, etc.

By our powers combined!

As you talk, you realize that by combining your speciality methods / rare equipment / other know-how the two of you together might be able to accomplish something new. Maybe the contribution is the sheer novelty, the creativity in coming up with it. Maybe you tackle a challenge that neither of you alone would have even dreamed of.

Hanging out is just fun!

Sometimes, what you do together can be something either of you could have done alone - but you work together, because that's more fun (and probably somewhat higher quality in the end, too). This obviously relies on a strong drive to collaborate, and may not be the motive you should aim for.

That's a great list.

I don't have any evidence for any of these observations, but I think most of the keys to a successful collaboration need to be in place before it starts.
1. Make sure you (and they) have the time and energy to commit.
The fastest way for a collaboration to fail is for one (or all) of the collaborators to be overextended. If you barely have time to do your own research projects, how will you be able to be a fruitful collaborator? I regularly politely decline collaborations where the time ask is too great. Likewise, it might sound exciting to collaborate with a Nobel laureate, but if they don't have time to actually bring anything other than their name, is that what you want?
2. The best collaborations are when the different parties contribute meaningfully novel expertise or resources.
Almost all of my successful collaborations have been a match between two or more parties that have expertise or resources that the other parties just don't have. The access to something collaborators can't easily replace motivates them to pull their weight in the project.
Examples:
- They are experts in a particular disease, you know how to use machine learning.
- You have interesting genome sequencing data, they know how to do de novo assembly.
- You have 50 participants with a rare condition, they have 10 more participants with that rare condition.
3. Make sure all parties are excited about the project.
I tend to avoid collaborations where the parties are not excited about the topic. I find that collaborators that just want to "pay the bills" so to speak struggle to deliver high quality products. The same goes for oneself. Is it worth it to work on a project that doesn't excite you just to get another publication?
4. Clearly agree on authorship and funding early.
This is key to avoiding drama later. Obviously, as the project goes on, things can change, but it's vital to have a baseline that everyone expects. One of the comments suggests that collaborations are frequently only productive when there is transfer of funds. That has not been my personal experience, although I admit that I have been privileged to conduct research in high resource settings.
5. Set realistic, measurable, and time-bound deliverables for each member.
The best collaborations, in my opinion, are when clearly defined deliverables are agreed upon at every meeting and the team members complete them or are agree to be accountable for why they can't be delivered. At the end of each meeting, we "go around the (often virtual) room" and agree to our "homework".
Examples:
- By next meeting, Ian will write a draft for the introduction to the manuscript
- By next meeting, Nasir will commit the code for training the deep learning model to the Github repository
- By next meeting, Linda will write aim 3 for the R01 submission
Also, it's important to schedule the next meeting before concluding the current meeting. For bonus points, the meeting could automatically recur.
6. Don't be afraid to end a collaboration if it's not working.
At some level, collaboration is like dating. They don't all work out. If you or your collaborators aren't able to meet or aren't able to make progress on the deliverables, I think it is reasonable and necessary to end them. I've started collaborations where others weren't able to meet when they agreed multiple times, and that just had to be the end. Sometimes collaborations can just drift off into the night, but sometimes you need to just say "I'm very sorry, but I don't think I can continue this collaboration".

I worry a bit about your #5. "Dividing up" the project is probably sub-optimal, just as it is in the creation of interesting software. Try to replace "you do this and I'll do that" with "we do this together". Hard, I know.

@Buffy I think it's interesting to see how people from different fields can disagree on things like this. To me, #5 is almost certainly the most important part of a collaboration. I can imagine that in theoretical fields, though, that such an approach might be counterproductive.

I kind of agree that #4 and #5 are probably good advice, although I've hardly actually done this, and had lots of great collaborations.

(+1) For both the scheduling (which seems relatable in my experimental field) and mentioning funding explicitly. When both parties are being paid to do something else entirely and have no prior collaboration history, joint projects can become notably hard to maintain.

It is harder to join someone else's project than to invite someone to join yours. Another idea is that you need to have more than "interest" in someone's work. You need to bring something of value to the conversation.
The conversations that lead to collaborations can be either local, at your university or in a small region with several universities, or at conferences where people with similar interests gather. But you have to offer ideas.
One way to make collaborations continue once started is to gather some funds to invite people to your place (or for yourself to travel elsewhere) to give talks on a topic of mutual interest and use the co-location to explore ideas.
A phone conversation or short zoom session isn't going to do it. You need to find ways to explore what each can contribute. That takes a bit of time. And once started, find ways to keep it going. This may also require some funds. At some point grants might be possible to fund co-located activities leading to collaboration, but it would probably be hard to do that as initial steps.


text:
How to politely tell a colleague they won't be an author of my article?
Looking for advice.
My coworkers and I have been working on a project for about two years, and we are right now in the process of writing a paper. About a year ago, we contacted a friend of mine who is also a researcher, told him about our results and asked for his insight. After a couple of zoom calls, he pointed us toward a research direction we had not thought about. After pursuing this direction for some time we found out it did not work, so we ended up doing something different which does not include our colleague’s suggestions at all.
Now, after discussing with the coauthors we have concluded that it is not fair for any of us to include this person as an author, since his contribution has been of no impact for the paper in the end. I agree with this decision. I also believe it would not be nice for our colleague to just find the paper online without any news, and especially since he is my/our friend I feel that I should send him the manuscript via email before sending it for publication. I wonder how I should I deal with the issue of authorship in this email: should I just send him the paper without him as an author? if so, should I explain to him that we do not think his contribution deserves it? or should I ask if he wants to be one? I am not familiar with the authorship etiquette…
Thanks in advance for your advice!

You used that person for free consultation. You should ask them what is the price for time spend.

If you are SURE that his contribution was not part of your paper, you can thank him in the Acknowledgment section of your paper, giving his professional association details as well.

His contribution was useful as it caused you to evaluate and drop your initial direction leading you to where you are now. I helped a colleague with a small part and was included in the acknowledgement.

The borderline for what constitutes authorship varies a bit by subject. What in some areas would be authorship in others earns an acknowledgement. A good place to start would be thinking about how likely they would be to be expecting to be an author.

https://academia.stackexchange.com/questions/155973/can-revision-authors-be-dropped-from-a-paper

Why do you think the friend expects to be a coauthor?

@SSimon: And who (and why) would pay this?

@user111388 OP who asked this

@Ssimon: Are you a researcher? Is it in your instition common practise that researchers pay out of their own pocket or charge money from other researchers (who presumably answer them while being paid for doing research)?

@Roland, I had no agreement, in our field it is common practice to share ideas like that. Maybe I should have talked about this in the beginning.

@Ssimon Nobody will pay for the publication, but if some expense comes up we will pay for it.

Thanks for all the comments! I think he will not want to be an author anyway, but since he is my friend and this is my first time having all the responsability for a project, I was in doubt

Why not consider the difference between "authors" and "contributors"?

Would you please mind sharing which field and country are you in? I find it a rather odd culture fellow researchers (and friends, while at it) expecting co-authorship just due to some insights. I've spent nights doing fully-fledged translations and reviews to help friends meet strict deadlines and all I've ever expected was them eventually returning the favour.

TLDR: Unless explicitly discussed otherwise, your friend does not expect to be a co-author. Just write a friendly email thanking them for the discussions and asking them if it is ok to include them in the acknowledgements.
As pointed out in the comments, this highly depends on which field we're talking about. In some fields, even pointing in a direction that actually turned out to be right might only be worth an acknowledgement (probably specific though, "we thank X for point to the results published in <obscure jounal 60 years ago>"). Since this is clear to most people, based on the information you provided, I would not think that your friend even expects to be co-author at all (unless they explicitly asked to be co-author, which would be border-line in terms of academic integrity but happens when the person urgently needs another co-authorship for formal reasons - in that case, however, one would expect that person to (a) communicate this clearly beforehand and (b) do much of the mindless busy-work, e.g. beautification of images).
That being said, I would assume that your friend knows they're not going to be co-author and thank them by email for the interesting, albeit ultimately fruitless discussions (but hey, maybe we can explore this in the future,...) and ask them if they agree to be mentioned in the acknowledgements. In other words, put a positive spin on it.

This. I would refuse accept authorship if I were the friend in this situation, and would probably find even an acknowledgement to be generous.

Thank you, this makes a lot of sense. We will definitely add him in the acknowledgments and since I know him, I also think he probably will not want to be an author anyway.

@photonQ glad to hear that, these situations can be very awkward. If you think that this answers your question, feel free to "accept" it - with the checkmark (so it will be marked as "correct" for others in a similar situation).

@user129707thanks for the tip about accepting it, I am quite new in stackexchange :)

asking them if it is ok to include them in the acknowledgements - I think this is a bit weird, though perhaps this varies by field or something?  I've never asked or been asked about such a thing.  I wouldn't even explicitly tell them I included an acknowledgement of them, but I might send a preprint and say thanks for their help.

I think that out of politeness, it is indeed a good practice to ask for the permission or notify the friend before writing their name in a paper, if it's not a citation. At least to be sure that their affiliation is correct for instance.

@photonQ Relating to that, don't forget that negative results are still results.  If it looked like a promising avenue but turned out not to be, make sure to document what it was and why, otherwise someone else will fall into that trap too.  And at that point it's natural to acknowledge your friend's contribution in making that suggestion, even if it didn't lead further.

@Kimball: From my experience (in pure maths/theoretical CS), asking people about acknowledging them isn’t required, but it’s common.  Firstly, it seems polite to inform people they’re going to be mentioned; and secondly, it feels more polite to phrase that as a question, than as just a statement.  (This may be a linguistic/cultural difference: phrasing a statement as a question, for politeness, is I think almost obligatory for most Brits, but is not so universal among Americans.)

If someone pointed me to a direction that turned out to be right, I might seriously consider offering them coauthorship or inviting them to work further on the topic. But that is not the case here. The suggested direction was not fruitful for the authors. But I otherwise agree with your thesis that the person probably does not expect to be coauthor.

@anemone Yes, but discovering that the proposed direction was not fruitful for the authors was fruitful for the authors.

@JeffE Well that is something that is not so clear from the way the question is phrased: "we found out it did not work" can mean that the authors can demonstrate that the approach is fruitless (which is how many people have read it, i.e., as a kind of negative result) or it can mean that it just did not work for them. I was actually wondering, when reading the post, whether anything would have been at all different had the OP not asked his friend for help in the first place.

@anemone The mere fact that the situation is unclear argues against the stance that the friend obviously should not be a coauthor.  Negative results are still results; not all contributions appear in the text of the paper.  I agree that the friend probably doesn't expect to be a coauthor, but I don't think the idea should be dismissed out of hand based on the extremely limited information we have.

@JeffE I am a stranger to the stance you quote. As a matter of fact I am not propositng anything, given I do not have enough facts. What I meant by "unclear" is "unclear to us from the given description of it". I assume there is a way for the OP to figure out whether the help of the friend had any other impact on the paper than the team spending time on the friend's proposal. In fact the OP presents us with a bit of an oxymoron, "the contribution was of no impact for the paper in the end". I commented on this answer highlighing the counterfactual case that a help would prove fruitful.

We made good progress pursuing direction X, but, it didn't work out due to Y, so we pursued Z, as described in the attached. We've included an acknowledgement of your input. Thanks again.

Under the circumstances, I doubt your friend expects co-authorship of the paper.  Your idea of sending a courtesy email to let him know of your proposed decision (instead of having him read the paper and see he is not on it) sounds like a good idea to me.  I recommend you write a courteous email thanking him for his help, letting him know that you and your colleagues discussed the issue, and letting him know your thinking on the matter.  Do not frame this as a decision that has already been made --- instead, offer him an opportunity to respond before you and your colleagues make your final decision, in case he disgrees with your views and wishes to make an argument for his inclusion.  Do not invite co-authorship unless you intend to follow through on that.
It is also worth noting that you could give your friend an acknowledgement in the paper rather than co-authorship.  If you decide that this is appropriate then you could offer this as an alternative to co-authorship.  If your description of events is accurate, the most likely response is that your friend will be happy to have had a chance to hear about the matter before seeing the published paper, and most likely he will agree that he has not done enough to warrant co-authorship.  In the unlikely event that your friend insists that he deserves co-authorship, refer the matter for decision to your Head of Department or some other third-party for an independent decision.

Often the process of discovery can take you down roads which lead to nowhere. It's sometimes useful to point that out, in case others question what was tried in reaching your ultimate destination. Though authorship may not be appropriate, and effort was expended and some kind of recognition for effort and contribution may be appropriate, it can often help others to understand more deeply when you leave signs along the way, "don't go this way, because of this."
Just a thought.

I'm afraid I have to disagree with many of the above answers. It does not matter whether the approach your friend suggested panned out or not. It matters how much work your friend did. If it took him 3 years of experiments to invalidate a promising approach, then yes, the contribution was valuable and he deserves a co-authorship even if the approach did not solve the problem. Of course, in the other extreme, a few minutes chat and a suggestion to try running the data through a different filter, may not merit authorship even if it has cracked the case.
Your colleague devoted time an resources to your problem. Consider the next time you consult him. Do you still expect him to devote the same amount of effort? Why?
Specifically in your case, a few zoom calls may not be considered a lot of effort, but the reason for not adding him as an author should not be "it didn't help", but rather "I didn't waste his time".


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Can someone who did mostly theory in academia survive in the tech industry?
I am currently seeking a career outside of academia (STEM) and every "entry-level" career in my field seems to demand familiarity with dozens of technologies.
For example, here is a data science job from a big tech company:

Familiarity with at least 1-2 popular AI/ML frameworks and tools -
TensorFlow, PyTorch, MXNet, scikit-learn, OpenCV, ARCore, and ARKit.
Expertise in estimation, experimental design, hypothesis, and A/B
testing.
Experience partnering with engineering teams to build and
test production systems.
Familiarity with AWS services such as EC2, DynamoDB, RDS, AWS Lambda, and Amazon SageMaker.

As someone who did an applied math degree geared towards the theory of optimization and learning, I am very familiar with how AI/ML works, but I am not too familiar with all these softwares. Furthermore, I don't know anyone who is currently doing similar research who plays with these Amazon cloud service regularly. It just seems there is no need to publish a paper using any of these tools.
It almost seem that you need an entirely separate graduate degree to fully meet these criteria.
By the way, almost all the jobs in the field are like this and this example is actually pretty mild and again not even for a senior role.
Can someone like me survive in industry?

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

Yes, you can survive there
By way of personnal background (so you know that I have some knowledge of this stuff), my career is as a statistician in academia and consulting, with the latter being in the tech field where similar requirements are often set out for positions.  Much of my academic work has been theory, but I've also been able to work effectively in the tech field and so I've learned a bit about it.
So, the first thing I've learned is this: These big lists of skills giving huge numbers of specific platforms are really just a "wish list".  Most organisations in the tech field cobble together the required skills by having teams of people who each specialise in different programs and platforms.  You'll occasionally run across people who have knowledge of most platforms on these wish-lists, but it is rare.  More common is to find people who have high-level specialty knowledge in one or two programs/platforms and who are able to learn the others on the job or collaborate with experts in those other platforms.  Moreover, expertise partnering with teams in the tech industry is something that you usually only get once you are already working in that industry, so new starters would not be expected to have this.
If you want to make yourself attractive for entry-level positions in the tech field, I recommend you pick one or two key programs/platforms and make yourself an expert in those things.  Learning a core language like SQL, Python, R, C++, or SAS (depending on where you want to go) will give you a foundation for programming work in the tech industry.  You can learn particular areas such as AI/ML if you like, but it is nice to get a broad and deep knowledge of at least one program/platform.  There are many online education providers that will teach you these programs much more quickly than getting a graduate degree (e.g., DataCamp, Udemy, CodeAcademy, Coursea, LinkedIn, etc.).
Once you have one program/platform you are really good at, you can gradually add other things to your repertoire until you have a more rounded tech resume.  This takes years to develop and it usually comes from being involved in projects where you have to learn and apply new tools.  (Indeed, you will find that even after you've learned them, you tend to forget programming platforms that you don't use on projects.)

I think this answers actually sets the bar too high. Being very proficient in a programming language is definitely helpful but OP already has a STEM PhD. That on it's own with say a month of self study is enough to get a good starting job in data science.  Being familiar with machine learning at an academic level is extra and puts OP already ahead of a lot of the competion.

@quarague As someone with a STEM PhD (and programming experience, and some self-study/independent projects) who has tried unsuccessfully to get a data science job, it feels like you are really overestimating how easy it is to get such a job. But perhaps I am just unlucky.

@quarague: It depends on the job. If you are applying for (say) a software engineering position, when I interview you, I am going to expect you to write code, and I am going to expect that code to (mostly) actually work. If you have a PhD in computer science or something, well, that's nice, but (perhaps surprisingly) it doesn't tell me whether you can code or will be able to learn to code in a reasonable amount of time. But I don't have experience with data science jobs, so maybe the standards in that field are different.

@d_b My comment was based on myself doing exactly that, pure math PhD with zero real world application, practically no programming experience plus one month of self study of SQL and R did get me several interviews and a job as a data scientist that I'm quite happy with.

@qurague: I think you're right about lower-level positions.  I suppose I had in mind the kind of positions where you can come in at a mid to high-level.  Also, does OP have a PhD?  I only saw him specify that he has an applied math degree (which I took to be undergraduate).

@Ben: OP was in a PhD program back in 2017, according to his profile. One would certainly hope that this led to a degree; it would matter quite a bit for the answers to this question otherwise.

You absolutely can do it.
This answer will focus on data science, since that matches the job description you posted. I know people with similar profiles who have made the transition. It will take some work to fill in some gaps in your experience, and to think about how to make your experience relatable to an employer. Even if you don't use the tools they are looking for, perhaps you can show you've used similar tools. And over time, you can learn the tools you need.
One thing to keep in mind is that many, probably most, people who do these jobs don't have PhDs. So it is not true that you need a graduate level education in the topics you listed [1].  What you do need is a working knowledge of common tools. It's useful to take one or two online courses that include actual coding examples so you can practice getting practical experience.
You should also be aware that (a) the "requirements" companies post are not strict requirements -- the ideal candidate will have all those things, but no candidate is an ideal candidate, so people are hired who meet a fraction of the criteria all the time and (b) people pad their resumes. You have to decide for yourself what you are comfortable with how you describe your skills on your resume. You shouldn't lie; you should be ready to explain and demonstrate any skill you put on your resume, and you should be prepared to have to use that skill on the job if you're hired. But, you also shouldn't sell yourself short -- you can pick up the basics of a lot of these packages before or while you are applying to jobs.
There is a major skill you have because of your PhD -- your ability to learn new things independently and use them to tackle unsolved problems. This is harder to quantify and put in a list, but it is very valuable to be able to do that in the business world.
One way to demonstrate your problem solving skills, and to learn the tools you need in the field, is to do some kind of personal project that you can do to show off your data science skills. A common first project is to compete in a Kaggle competition, or do a previous competition, and be ready to talk about the decisions you made analyzing the data. However, if you can find something that stretches you beyond this, that is even better -- Kaggle lets you demonstrate you can fit a model to data, but it doesn't include many steps that are part of the bread and butter of a data science job, such as gathering and cleaning data, and deploying a model so it can be used for something.
Finally, there are data science bootcamps that exist to help people in academia transition to data science. Your mileage may vary on these... On the one hand, they claim to be able to place people into very good jobs. On the other hand, they are expensive and you will have to go for several months without a salary. But, they are an option to consider as well if you are having difficulty landing a job.
None of this is to say that you won't have to work hard. You may need to spend a significant amount of time on your resume, practicing for interviews, and sending out lots of resumes (it's a numbers game -- most places won't respond to a resume they receive without a referral, but some will if you have a good resume). You may not succeed at first. But it is possible with dedication.
There are a lot of resources online for people looking to make this exact transition. It is possible to get too far into the weeds with them, but do take a look at stories of people who went into industry and practical tips for getting into the field you are interested in. I also urge you to draw on your in-person network as much as possible, both for advice (if you know people, or know people who know people, in the industry you want to work in) and for emotional support. Looking for a job, and changing fields, is tough, and you won't get a lot of direct support within academia.

[1] Something one of my friends pointed out to me is that in academia, you spend all your time around PhDs, but most people outside of academia do not, and find a PhD to be impressive! Now you don't want to take this too far and rest on your laurels, especially since just the fact of having a PhD by itself will not convince anyone to hire you, but do keep in mind you have some real strengths that may not be obvious to you because of the environment you find yourself in. You are going to need to recognize these strengths and convey them convincingly to potential employers.

"the "requirements" companies post are not strict requirements" Unless they're using an automatic system to throw people's resumes in the rubbish because they don't have specific keywords in them, because that's cheaper than having a human actually look at the resume and make a decision about whether or not to throw it in the rubbish.

@nick012000 Well, this is where "padding your resume" and "describing your experience in a relatable way", and "it's a numbers game", come in. Your resume needs to get you in the door past HR talking to the actual people you will work with. At that point, you can explain your expertise, and you can definitely be hired even if you're not an expert on every one of the 20 technologies listed in the ad. The point is, don't rule yourself out based on the job description, if you think you can do the job.

To add onto @nick012000, there's a saying in the tech industry: "If you meet all the requirements for a job, you're overqualified." Yea you should learn programming concepts and different languages to an extent, but year-based experience requirements are almost always non-realistic.

I've lived this myself.
I got a Ph.D in Electrical Engineering, and mostly did linear algebra (math) and software simulations with Monte Carlo statistics.
I can't fix your toaster, do wiring, etc.
So for me, I did audio firmware which sort of double-counts as EE and software engineering, for about 5 years.
I eventually wound up becoming a software engineer, and there was a 'happy ending.'
It would depend on your own skills, of course.
Someone who has a lot of technical ability might still do a theoretical Ph.D.
But in general, this is a known problem, and can cause difficulties in the industrial world.

I am very familiar with how AI/ML works, but I am not too familiar with all these softwares... It just seems there is no need to publish a paper using any of these tools.
It almost seem that you need an entirely separate graduate degree to fully meet these criteria.

You don't need a degree to meet these criteria, you just need to learn on the job for a couple years. The job postings are hoping you'll do that on-the-job training on someone else's dime and can hit the ground running, but that rarely happens in the real world. Everyone's first day, from the most senior to a fresh graduate, is spent figuring out how things actually get done at the new shop; HR doesn't seem to understand this.
I'm a computer engineer by education, and a controls engineer by trade. I'd never used a Rockwell PLC in school, but I had a thorough understanding of boolean algebra, and ladder logic just isn't that complicated.  When I first started, I didn't know how to calibrate a load cell, but I could have built one from first principles, and that was enormously helpful when diagnosing measurement artifacts. Academics typically try to understand the how and why, while industry is more likely to value pragmatism - just make it work and don't ask questions. This will make you less effective on easy assignments, but work in your favor when the team runs into a difficult problem. You just need to find a team that will help show you how to get started - ("Here's your AWS credentials, here's how you log into your EC2 instance, here's how you install TensorFlow, and here's how you make a neural network to classify shapes as triangles or squares"). Realistically, that's just about every team, you're probably not ready to start as a freelancer yet but any engineers will be happy to help you get past that tedious part of the learning curve.
You'll just have to find somewhere that will let you start working without checking those particular boxes. At big companies, you may have to bluster past some of the HR drones ("Of course I meet the AI/ML requirements, I implemented [subsets of tools like] those from scratch in my CS435 coursework") to talk to someone who can actually determine whether or not you meet the requirements. Also, I expect that some of those job postings probably require 5 years' experience in technologies which are 3 years old - and yet the positions will be filled.

+1 -- "some of those job postings probably require 5 years' experience in technologies which are 3 years old". Too real.

Even if you have extensive experience in a technology, that doesn't necessarily mean that you're going to be able to drop into an existing project using that technology. Someone with years of experience in C++ GUI applications with can end up totally lost if his previous environment used Qt and CMake on Unix and his new one is GTK and Visual Studio on Windows. Every project will have its own implicit knowledge base that you can learn only from the project itself. The best way to make this learning faster is to have good general knowledge, as with the Rockwell PLC example in this answer.

@Andrew I kid you not I auctually saw an add for a intermediate Blazor developer just today. Microsoft committed to long term support of the technology literally 3 months ago.

Programming was a second career I picked up out of necessity --it had previously been a hobby. My degree was in the humanities (philosophy). In about 10 years I've advanced steadily from an entry level position to being near the top of the career ladder at my company. My story is not unusual. Just recently I had a conversation with a colleague who was a bus driver two months ago before taking a crash course.
Although companies often list long alphabet soups on their listings, the fact is that the job market is white-hot right now, and there are far more job openings than qualified people to fill them. Take the time to get the base level skills, and someone will take a chance on you, no matter how little experience you have.
The other factor to remember is that technology advances relentlessly, so even people who are well-qualified today still need to constantly acquire new skills. That means you're not really so far behind anyone else. If you are a good and disciplined self-learner, there is no reason you cannot thrive in tech.

"the job market is white-hot right now and there are far more job openings than qualified people to fill them" Where do you live? The opposite is true down here in Australia. There's way more unemployed people than job openings.

@nick012000: OP is from the Netherlands, I am too. White-hot is probably an understatement, unemployment is at record lows industry-wide. I'm working for 2 AI companies, and both are actively hiring. I think that the AI field is even more short on employees. Ironically that is part of the problem for starters; a new inexperienced employee will require significant coaching.

I'm in America. But more and more companies are hiring remote, so geographic location is becoming less important.

Job posting are often written by clueless HR personnel often times with the best of intentions don't know what any of the terms mean. You don't have to be the best candidate they would want, you just need to be the best candidate out of the pool of replies.
Most job postings would say something to the effect that the employer reserves the right not to make an appointment but often times this is just posturing. In South Africa it is common for tech jobs to remain vacant for months on end. A lot of tech companies growth is largely dependant on how many reasonably qualified people they can get to do the work.
Also I do think it worth noting that because the tech scene is so generally lucrative you get a lot of businesses who are willing to take a chance on employees just because the upside is so big.
What I would say is that coming from the academy is you have to realise is that you are not selling programs or software. Nobody cares about the intellectual enterprise of programming in the business world. You only have value for a business in how you effect there bottom line.
If you can prove to a business owner that he can make 50K in profits by either reducing expenditure or increasing income by buying  your 20K piece of software then you are going to make loads. This may be a simplistic example but you have to always know that in this programming world you must solve some sort of problem or fulfill some sort of physical or emotional need of a business or an individual.
Your programs are only valued in the problems they solve. This is how this world operates. It is not about the advancement of human knowledge. It is about the advancement of your bottom-line. Too many times I see these so called venture-capitalist sell some sort of tech idea with not even a hint of how they think it is going to operate in the real world and I cannot help but think to myself are they trying to sell me shares in a company or are they trying to recruit me to some sort of new age cult that they are trying to found.
Just have what problem you are trying to solve be in the forefront of everything you do.

I had to do a double-take when you said AI/ML to even say I recognized one of the acronyms and tech buzz terms. First job in tech was 22 years ago at age 35ish with a high school diploma.
Everyone I've worked needs a quick-study, problem solver. If you have social skills, even better.
I've been blessed with many opportunities, and many I wasn't qualified when I walked in the door. But I got that way as quickly as possible. Consistently churn out work, own your mistakes, and try to get along with folks. Don't set your sights on the moon for your first position. Mine was making some html conform to a different template. Made a deadline and worked ~3 years at that one "6-week project".
I also worked 4.5 years at a university. It's where I learned that PHDs don't know everything (childhood misconception). Some of the academics were nice, humble, regular guys/gals and some could be challenging. I think most were petrified they couldn't make it in a real job. At least not with the awe-struck looks they were used to experiencing from kids. It was nonsense, they could have done it.
Yeah, I think you'll do fine. Tech is big, broad and getting bigger by the second. Get on in here! :D

I do entry level recruitment for the technical division of a large company and the features that we look for in a successful candidate are:

A numerate degree in a scientific or engineering discipline.
An obvious interest in the technical field for which we are recruiting.
Good problem-solving skills.
The basics of at least one programming language.
Not being insufferably annoying.

At this level we are looking for raw talent, not an intimate knowledge of the AI library du jour. Although, if you said that you had done a course in (say) Tensorflow then it would certainly help your chances.
Frankly, the skills that you list would match one of our engineers with at least 5 years of experience and if a company is offering an entry-level role to someone with that amount of knowledge then they do not understand your value, or their business.

Point 5 being negotiable depending on brilliance. You can be a drag to be around but then you surely have to be great at your job.

My answer is slightly different: Yes you can do it, if you are willing to adapt.
No one expects a new hire to hit the ground running at full speed---for every job there's an expectation that it will take some time for you to learn the environment, the culture, and the requirements. The toolkit you develop in a PhD program may or may not align perfectly with a given job, but the ability to independently learn and expand your own skills will help you to get up to speed quickly, as the other answers have said.
However, I've had a lot of experience in hiring former academics, and the one big area that I've seen issues is in adapting to the culture change. I've met many, let's say, high-maintenance PhDs---those who expect to exclusively do the pure, challenging, technical work, and who scoff at the soft skills aspects of the job. Working collaboratively, against deadlines, tackling the problem in front of you instead of the one you wish was in front of you, contributing to meetings and strategy are all areas where I've seen PhDs struggle, not because they can't do the work but because they don't want to. I've met plenty of hiring managers who see a PhD as a red flag because they assume that the applicant will not be willing to adapt.
Often, using a cover letter to explain why you're interested in the position and showing that you understand and are eager to do the work will go a long way.

I agree, it is not what pet technology you think is rad. It is about what your employers needs you to use. I personally think Blazor and the .NET interpretation of Web Assembly is going to make Angular and co obsolete eventually. I would still not hesitate to learn any current SPA model if someone paid me a salary while I worked and learnt it.

I did a PhD in theoretical computer science and I'm currently a (senior) software engineer at Facebook.
This is counter-intuitive, but after a PhD, interviews at bigger companies are easier than those at smaller companies.

Smaller companies often look for specialists. You are expected to be an expert in the tech stack in the job descriptions. On the contrary, at big companies, e.g. FAANG, they often look for generalists that can do anything, so the interviews are heavy on algorithmic coding questions. They are not easy, but you know all 7 or 8 topics in advance: tree, graph, binary search, dynamic programming (very rare) etc.
At smaller companies, a PhD may be considered as disadvantages. On the contrary, big techs love PhDs. With 0 experience, Google will start PhD holders at L4 (median salary $270k per year in the SF Bay Area, see levels.fyi/), while new undergrad starts at L3 (median $192k per year). Interview for L4 at Google include 4 rounds of coding questions. Nothing else.
If your PhD is relevant to the work they are doing (ML is highly relevant everywhere), you will have easier (and maybe fewer rounds) coding interviews.

This post proves that I need to start applying to Google and move to SF.

Speaking from people I knew in college, many physics and math majors, including Masters and PhDs, had no trouble ending up at tech jobs such as programmer, data scientist, or in quantitative finance. Companies have different hiring strategies, but many interviewers look for general problem-solving ability and reasoning skills, and are willing to look past not knowing every programming library as long as the candidate has a baseline programming/command line knowledge and shows eagerness and ability to learn (For example, if you have a basic familiarity with linear algebra, something like numpy should be pretty easy to pickup).

Sure, if you are willing to learn those technologies, and not treat coworkers as servants that convert your PhD magic into code.
It is very rare that a PhD has enough skills/value to the company that he can continue doing the equivalent of research without doing "boring" stuff like coding/cloud stuff.
In other words if you can get PhD you easily have ability to learn the tech needed, but the issue here is if you are passionate about learning the various tech stacks as you were for the work related to your PhD.