Archive | November, 2013

11 things you should look for in a postdoc position

26 Nov

If you were a successful PhD student in biology (meaning you finished in reasonable time with at least one first-author publication), you should be able to find a postdoc position without too much trouble. If you have a hard time finding a postdoc position I think there is likely something wrong with how you search, rather than your qualifications. The reason is simply that there are many postdoc positions out there. This also means that if you want to get into a great university, your best chances are to do so as a postdoc. And if you are applying for a fellowship (HFSP, German Science Foundation etc.) you’re chances will be higher if you apply to a very well-known university.

How to choose

OK, so if things are going well, you may have the choice between several postdoc positions. How to choose?

I think you need to evaluate a possible postdoc position seriously and on many different dimensions. Someone may tell you that all you need is a project you’re excited about, but I think that is not true. A great project is important, but it is not everything. I decided to make a list of eleven things that I think are important. Ideally, your postdoc position will score high on all dimensions. But at least, it shouldn’t score very low on any of these dimensions.

So here’s my list:

  1. You need to work on a topic that excites you. In some cases, you may have a precise project in mind when you start your postdoc, but in other cases, you may just know that the lab you’re going to work in works on exciting things. If the topic you work on doesn’t excite you, your postdoc will be boring. It will be tough in many other ways, you don’t need boring in the mix.
  2. You need an advisor who can and will support you. She/he should be influential, in the right field and willing to support her/his postdocs. Did her/his previous postdocs land good jobs at places that would be interesting for you? If you want to work with someone who is very junior, consider being co-advised by someone more senior.
  3. You need an advisor you get along with and whose science you love. Spend enough time with her/him to make sure that you like the person and the scientist before you decide to join the lab. Ask if you can sit in on a lab meeting, so that you can see how the people in the lab interact with your potential advisor.
  4. You need to be in a lab that is central to the field that you want to be part of. If you tell people “I am in so-and-so’s lab,” they should know who you are talking about. This will help you to become known, to get your papers published, to get your talks accepted and to get a faculty position later. Avoid being a “scientific orphan”[1] by switching fields to often or too drastically (although being an orphan is better than being in the wrong field for the rest of your career!). (At the same time, you should try to carve out a niche for yourself that is different from what your advisor does, but I think you can start thinking about that when you’re two years into your postdoc.)
  5. You need a lab that is fun to work in. Find out by talking to people who are or were in the lab and ask them what they like and don’t like about the group. Do lab members collaborate or help each other? Are there lab meetings? Is the group too small or too large for your taste? Do the lab members have lunch together? Or go out for drinks together?
  6. There needs to be the possibility to publish multiple papers (although in some fields, the only thing that counts is to have one big paper in Cell, Nature or Science). What have the other postdocs in the lab published in previous years? If they didn’t publish much, it will be hard for you to publish a lot. Can you collaborate on ongoing projects that are likely to lead to papers? Does your project include certainly publishable parts?
  7. You need secure finances. You need a salary, money for experiments, and money for travel to conferences. It’s hard to be creative if you have worries about money. Ask your potential advisor and ask other people in the lab. Did anyone in recent years have to leave the lab because there was no money left? Try to bring your own fellowship, this will buy you freedom.
  8. Freedom. You need to be free to have your own projects, by yourself or with other people. You need to be free to decide whether to work in the weekends or not. You need to be free to decide which conference is of interest to you. You need to be free to decide when your paper is ready to submit. Ask others in the lab whether they have enough freedom.
  9. You should go to a well-known university. This will look good on your CV, and it will help you find collaborators both inside the university (because there will be some great scientists at this well-known university) and outside the university (because people will more likely want to collaborate with you if you are at a well-known university). The name of the university is especially important if you consider leaving academia at some point.
  10. You need to be in a nice place to live. If you hate the city, don’t go to New York. If you dream of living in New York, there is no better time to do it than now. You’ll most likely be spending several years in your postdoc.
  11. This was going to be a list of 10 things until someone reminded me that you also need to be in a place that has the equipment you need. You should not have to spend your time fighting for space in the greenhouse or nodes on a cluster.

Is there anything that should be on this list that I forgot about? I’d love to hear about your ideas.
I hope you’ll find a great postdoc position and enjoy your postdoc years!

Oh, one more thing:

Leave if you need to

If you’re already in a postdoc position and you find out that you’re in the wrong place, know that you can quit. I know several people who quit a postdoc position after a short time and found a much better one afterwards.

Thanks to Nadine Vastenhouw and Oana Carja for comments on an earlier version of this post!

[1] The term “scientific orphan” was taken from a talk by Sheri Simmons (Woods Hole)

How to analyze 80,000 HIV sequences?

2 Nov

A few months ago, Bob Shafer asked me if I wanted to work with him and Susan Holmes on an editorial for Journal of Infectious Disease. Bob Shafer is a well known Stanford virologist and runs the extremely useful Stanford HIV Drug Resistance DatabaseSusan Holmes is a statistics professor at Stanford. Our task was to write an editorial for Journal of Infectious Disease about a recent paper by Joel Wertheim and colleagues.

Start with 80,000 sequences

The paper (and also our editorial) deals with the following question: how can we analyze the large numbers of HIV sequences that are available in databases to learn about the global epidemic? The basic idea behind analyzing these viral sequences is that each sequence stems from one HIV-infected person and by analyzing the genetic relationships between the  sequences, we can learn something about the global HIV epidemic. For example, if person A infects person B who then infects person C, then all three of them are expected to have very similar HIV strains, with very similar sequences. If genetically similar sequences are usually found in the same country, then we could learn that the epidemic spreads more easily within than between countries.

Build a tree

Traditionally, such analysis of HIV sequences starts with building a phylogenetic tree of the sequences. However, building trees is extremely hard if the sequences are recombining (as in HIV) and if there are a large number of sequences. One common solution to these problems is to remove recombinants and analyze a subset of the sequences. However, this means that we lose a lot of information.

Or not

Joel Wertheim and colleagues decided to take a very different approach. They started by calculating the pairwise genetic distance between all sequences. Viral sequences that are close to each other (with low pairwise distance) must stem from people who are close to each other in the epidemic. Next, they created a network by connecting all pairs of sequences that were less than 1% different from each other. The resulting network could be analyzed by standard network analysis techniques. The authors were thus able to study the relationships between 80,000 worldwide HIV sequences. They found a surprising number of international connections between the sequences.

Limitations of the network approach

In our editorial, we argue that the network approach is worth exploring, but it has its own issues too. For example, we write that “many more connections may be inferred than could have possibly existed in the real transmission network (…) For example, if multiple infections happen in a short time span, several people may be infected with very similar viruses. The viral sequences from these people would all be connected by the method of Wertheim and colleagues, leading to many more edges in the thus constructed network than exist in reality.”

It is not clear how these additional edges (connections) influence the results of the analysis. And as long as we don’t know that, we need to be very careful when interpreting results from network analyses based on sequences only. It’s clear what needs to be done!


Pleuni S. Pennings, Susan P. Holmes and Robert W. Shafer. HIV-1 Transmission Networks in a Small World. JID. 2013.

Joel O. Wertheim, Andrew J. Leigh Brown, N. Lance Hepler, Sanjay R. Mehta, Douglas D. Richman, Davey M. Smith and Sergei L. Kosakovsky Pond. The Global Transmission Network of HIV-1. JID. 2013.