A long while ago (my gosh, 13 years!!) I wrote on this site a two-post piece titled "Five Tips for Particle Physics Ph.D. Wannabes". At 43 years of age, I felt confident that I could look back to some experience gathered while being a Ph.D. myself, and later on while advising others. I believe the few advices I put together there are still mostly valid today. Have a look if you are a grad student in search for tips!
Today, though, I feel inspired to target the work of post-doctoral researchers. I am older than I was when I wrote those two posts, and I have seen things along the way. But am I qualified to give advice at all? After all, my personal career is not an example per se - I got tenure relatively late, and although my bibliometric indices put me well in the top-1% scientist category, I all too well know that is a quite biased statistic for particle physicists. In terms of shortcomings as an advice giver, you name it, I have it - I had my setbacks, delays in career steps, failures in grant applications and research goals.

But in fact, the above is exactly the reason why I feel perfectly qualified to write about what works and what does not - or at least try to. The rationale is that if your career is a perfect trajectory rising to the stars, studded with jumps from a success to the next one, you are quite likely to not understand very well the struggle that the rest of us have to fight, and chances are you do not actually understand which are the strategies that work and those that do not. For they all worked for you!

This concept should be very immediate to grasp for anybody who has had some training in machine learning, as there is a quick analogy we can draw. Imagine you are training a model (it could be a neural network, e.g.) to learn how to achieve some goal. If you give the model a very large amount of data to work with, the data will be more than sufficient to let it achieve the goal close to perfectly. You will then have a harder time to figure out what really are the winning strategies for maximization, then if you had worked with a smaller, noisier dataset where the job of the NN would have been considerably tougher. More cunning would have been required in the second case, and the learning process would have turned out to be more effective. 

So, without further ado, let me list a few suggestions I have for freshly brewed Ph. D.s, who move their first steps into a post-doctoral position. I hope they will help! But if you really are interested in the issue, you should also read the five tips I pointed to above...


1. Be there, be available

Scientists are always too busy. They focus on their research when they can, but they also have teaching obligations, administrative issues to fix, grants to write, conferences to attend, conference proceedings to write, articles to review, articles to draft. And meetings, oh boy - meeting after meeting we grow old without realizing how much time we wasted there!

As a fresh Ph.D. graduate moves into her first post-doctoral research position, she will find out that the pressure of having to graduate and deliver a thesis does not give way to a more livable working condition - pressure only increases as you move up in the world of research! All those extra activities will tend to subtract time to the important tasks on which she is building the bases of her future career advancements - research. 

The reaction that many of her colleagues have in that situation is to freeze some of the degrees of freedom: they become unresponsive to less-than-urgent emails or queries, they do not show up at seminars, they become hard-to-catch fish. This is very understandable, but on the other hand it backfires in the long run, as if there is something more important than scientific achievements, when it comes to evaluating the career of young researchers, it is knowing they exist, have a face, and interact positively with the environment. 

The bottomline is that participating in group's activities, offering your time even when you don't have much of it, and resisting the temptation of isolating in your own work is very important, and it will provide you with more connections, more esteem from your colleagues, and a more positive environment around you. 

Another good habit is to answer your emails immediately. Your colleagues will appreciate you for that, and taking two days to answer will not make you save time. Procrastination is never a good idea when other people are involved!

2. Claim credit

In the academic world it is rarely the case that people actively look for ways to appreciate your work - in fact, the opposite is true. This is because that in order to outshine their colleagues - which may help with career advancements - they must  not let the brightness of competitors manifest itself. So regardless of whether you want to conform to that annoying habit of downplay other people's scientific merits, what you must ensure is to not be afraid to proactively clarify, whenever it is not awkward, your scientific achievements; publicize them, and let them be visible to the research community. 

A good advice is to spend some time in putting together a catchy personal web site where you put in evidence your successes, and link it broadly, by e.g. putting the link on the footer of every email. Use social media to advertise new papers or talks. Other useful tips: hang things outside your office door. I even have a small table outside my door, where I put a hardcopy of all my new publications, and I make it clear they are free for grabs. Strangely enough they do get picked up!

It is also important to make sure, when people talk about some problem or research question that touches on topics you have worked on, to bring up your own contribution, if relevant. Do not assume your colleagues know what you have done - you don't know so well their own works, do you? Yes, this may mean exerting some violence on your otherwise unassertive behaviour and shiness. But those character traits are your enemies in the academic world, and you need to suppress them a bit. 

3. Give credit

On the other hand, it is a very good habit to also try and give credit to your colleagues when it is due. This will prevent them from getting the impression that you are full of s**t by being too much into advertising your own work, as suggested above. If you work with collaborators, your slides should always carry the names of all of them; and if that turns out to be impractical, the slides should not even have your own name - find a name for the group instead. 

When you give a talk at a conference, be sure to explain who your co-authors are and what is their contribution. A good practice is to have a slide with mugshots of your collaborators next to their names, and to spend a minute spelling those names out when you give the talk, highlighting their roles. This will make for a healthier work environment, where everybody feels more appreciated. It is extremely easy to err on the unsafe side in similar circumstances - even an extra word in the direction of evidencing your contribution to a collaborative work, or one word too few describing other people's contributions, may become ground for some untold hard feelings they may cultivate against you - and you certainly don't want that.

4. Accept all students

I remember well my experience with looking for a thesis topic, as an undergraduate student in Padova. I did not have a clear idea of what I wanted to do, but I liked particle physics. My father, who was a art historian and a professor at the University of Venice, advised me to visit Alessandro Bettini, a professor in physics whose father, Sergio, had long time before been the thesis advisor of my father. Given the high esteem my father had for Sergio Bettini, he insisted that I should ask Alessandro for a thesis topic. It seemed such a pleasing repetition of a successful story...

My father's suggestion and hope went south. Alessandro greeted me with much less warmth than I expected given the circumstances (I had clarified beforehand the indirect connection we had), and he was not in the least interested in taking in a student who did not have straight A grades. I still recall that meeting with a bad feeling. Luckily for me, I later found a great advisor (Dario Bisello) and I went on to work with his team on the search for the top quark with the CDF experiment at Fermilab. I honestly think I brought value to the group and my work was useful, so maybe Bettini should have known better?

The reason I bring up that episode of my own experience as a student is that ever since then, as a post-doctoral researcher, and then a researcher and professor, I have made all efforts to accept all students that offered to work with me or my group for their thesis topic, encouraging them even when they seemed disillusioned about their future chances to pursue a career in research, and clarifying to them what my own trajectory had been - the one of a student who had taken a few extra years to graduate but who later found a way to prove his worth despite the not-so-great grades. 

Does that strategy bring fruit? I know a lot of colleagues who are scared by the time they have to "lose" to train young collaborators, and they often reason that usually advising an undergraduate student is a negative-sum game except for very rare cases when the person appears exceptionally bright. 

Quite on the opposite side of this argument, I reason instead that it is never lost time to devote time in training the younger generation, and that being consistent in this habit will pay back. First of all, you should not assume you can assess the potential of a student -one thing are the grades they have in exams, and a very different thing is how well they code, how independently they think, and how much effort they are willing to devote to the research problems you offer them. 

Furthermore, by being kind and inclusive you will do a good service to your university, and you will fulfil your duties as an educator. And finally, a "bad" student (there is no such thing, by the way) will often bring in good ones, as students communicate and your good attitude will become known among them. 

5. Publish a lot

I know - what a no brainer, right? We all know the centuries-old rule, publish or perish. But in fact, in the field of experimental particle physics (and a few related ones) a young researcher does not necessarily have to worry too much about his or her publication rate. That is because they are embedded in large collaborations, and thus their name appears in the author list of dozens (or hundreds) of articles per year regardless of what they do. So, should they not worry about conceiving and bringing to publication stage scientific articles of their own making, except that one very long-term analysis project they are working on within their group?

Well, first of all, bibliometric indices are on the move. While the H-index rewards your belonging to a large collaboration, with the high throughput and citations, there are others today which start to pay more attention to the individual contribution of each author. How? By giving more weight to review articles, by distinguishing whether they are first or last in the author list, by dividing the number of articles by the log of the number of authors. 

Also, selection committees who have to sort out researchers in experimental particle physics today pay very little attention to things such as the H index, and much more to the presence of "independent thinking and initiative", which can be gauged by the presence of a set of extra publications that the candidate has produced with a restricted list of co-authors, or alone. 

One very concrete possibility of caring for that aspect of your CV is to look for spinoff activities. The field of Machine Learning provides a very clear example: if you are a data analysis in a large particle physics experiment, e.g., you are almost by force going to become an expert in the use of machine learning tools. If you pay a bit more attention to that area of research than that, you then become capable of producing independent, original works: a new algorithm, together with its testing on experimental data, makes for an excellent topics for a scientific publication. Statistical methods also provide ground for spinoff publications for data analysis experts. And of course, those of my colleagues who are more into hardware development have been using their expertise and their novelties in designing performant particle detectors as the perfect platform for publishing few-names papers of high impact.

6. Apply for grants!

Another no-brainer? Well, again, it depends. Many of my colleagues, in experimental particle physics, never write grant applications. They do not need funding, as funding is automatically provided to the experiment they work on. And many who live under that umbrella have the feeling that competing for grants directed at all sciences in general (such as ones offered by the European Community - ERC, Marie Curie, etc.) are doomed to fail because the reviewers will not like to give funding to research which is already so well oiled. 

Although there is some truth there, it is not enough of a reason for not trying. Winning a grant is a very significant achievement, which will often boost your career, make you more visible in the community, and last but of course not least, give you extra funds for hiring collaborators, organizing events, widen your network, and do better research. 

However, this one comes with a largish catch. Writing an application is an activity that you need to take very seriously, or it will fail. The highly-competitive nature of the selection of winners implies that you must give everything you've got to it, if you decide to write an application. It may take 100% of your time for two or three months, in typical cases. Is it worth doing it? Well, I think it is, if you want to give yourself a chance to grow. 

Of course there are other good ways to spend three months of your working life - write that new paper you have half-finished in a folder of your desktop, for instance. But writing an application has also beneficial effects even if you end up not winning. First of all, the material can be reused for a future application, when you will have benefited from the criticism of your revisors. And it can also provide the basis of networking, because in many cases the application will have forced you to create connections with other researchers, and these connections are likely to be useful in the future.

7. Networking!

Connected with what I touched on above is the issue of widening your research network. Again, it all depends what research you do - in some cases you need not do much, as the network existed before you joined, while in others you start alone and have to build it up. My experience as a physicist is that I started with a large network of colleagues with whom I had a chance to collaborate in a large experiment (CDF), and this helped a lot when the time came to build a network for a grant application we ended up winning.  But I have since then continued to widen my contacts, expanding to the computer science community and the statistical sciences. It is sometimes a bit time-consuming to keep in touch and find the time to discuss potential projects that at the end do not concretize, but overall the balance is largely positive, and you should invest in that direction.

In fact, my attention to networking has brought me not only an expansion into common endeavours with computer scientists and statisticians. Since last month I am the president of an international organization (USERN) which includes scientists from _all_ disciplines (21, to be exact). The chance to get to lead USERN, a network of over 21,000 members, with an advisory board of over 600 top-1% scientists, came from being sensitive to this aspect of a researcher's work. When USERN asked me to contribute to various projects I did it with enthusiasm, although it did not seem to be something that would lead to direct scientific dividends, but maybe only to help an institution that aimed at goals I shared (internationalization and interdisciplinarity of scientific research and education). 

8. Broaden your interests

Becoming a world-class expert in a narrow field of study is a strategy for becoming a well-known scientist. But it is not the only way. Expanding into different areas, by acquiring skills that do not belong to those typical of your specific field of research, is also an effective way to have an impact and get recognition. Nowadays, interdisciplinarity is highly valued and it is key to access funding. But it is also a way to reshape the way you think, giving you the expertise to look at problems from a number of different sides. In other words, you give yourself a chance to think outside the box. 

Besides the above, I already discussed at length the benefits of publishing in different areas, by leveraging the connections of your research to tools and methods from disciplines that may become your next expertise goal. Life is long, and although you were born a researcher in X it does not mean you stand no chance to become better known for what you will contribute to Y!


Ok, I think I will stop here. Maybe many of these considerations are useless - nothing works for everybody. But I am sure you can find something helpful in the bulk. Good luck!