Applying for postdoctoral positions in the sciences (part II)

Last week I began a list of things I learned from my recent experience applying to postdoc positions — here is the second half of the list.  As I mentioned in the previous post, keep in mind that the process can vary a lot across disciplines, besides the fact that even in the same field different people can have quite different experiences.  So this just represents my own experience in biophysics, but I hope it will be useful to someone else!  We will start the second half with what I think is one of the most important points…

  • Have alternative plans.  I once heard a professor claim that people should only do a postdoc if they are “academia or bust,” and it really irritated me.  There is no “or bust” in life — even under the best of circumstances, there is always a chance things won’t work out the way you wanted, and we all must have alternative plans for every aspect of life.  Do think carefully and realistically about your career goals and whether a postdoc is a good fit, but even if you decide a postdoc is your first choice right now, it should definitely not be your only choice.  (Corollary: doing a postdoc because you don’t know what else to do is usually a bad idea.)  So spend some serious time contemplating what your next moves will be if the right postdoc doesn’t work out.  Even if you end up doing a postdoc anyway, careful planning now may pay off if you arrive at a similar juncture later.  But moreover, knowing that you have other options will make your whole application experience much less stressful.  You can rest easy knowing that even in the worst-case scenario for your postdoc search (i.e., no offers), you’ll have other options and life will go on.
  • But still be persistent.  Don’t give up if your first few applications or inquiries go nowhere (of course, having those back-up plans will help to make this less discouraging, too!).  Unfortunately, many applications or inquiries to professors receive no response.  If you are just contacting individual professors asking if they even have a position available, I think it’s worth sending a follow-up e-mail after about a week if you don’t hear from them.  If you’ve formally applied to a group or fellowship program, you may need to wait a few months to hear back, although I think it’s still worth following up at some point if you haven’t heard a response.  If someone really isn’t interested in you or just doesn’t have an opening, you deserve to hear them say so.
  • Be prepared for your visit/interview.  After applying, you may get invited to visit the group or department.  Sometimes you’ll give a formal research seminar to the whole group; other times there is private interview with just faculty.  The Graduate School-New Brunswick has held workshops on such interviews in the recent past, and they are worth attending.  You also usually have a series of meetings with faculty, current postdocs, and possibly grad students.  Besides having ready a good spiel about your research and career goals, do your homework on the people you’ll be meeting.  Make sure you know what kind of work they do, and plan some things to discuss with them.  Of course you may discuss each other’s research in these meetings, but they are also key opportunities to get inside information on what the group is like and whether you’d be happy working there.  Don’t discount the meetings with the postdocs and grad students.  Besides the fact they can give more honest feedback on the working conditions, their advisor may ask them later what they thought about you, so try to leave a good impression.
  • Negotiate.  Once you receive a formal job offer, you should go over the terms of the contract carefully and consider what is negotiable.  Salary and the length of the contract are obviously important, but also find out about health insurance, access to funds for travel and equipment, if they will help you with relocation expenses, employee privileges (can you use the campus gym?), and any other benefits.  My understanding is that salary is usually not very flexible for postdocs (since salaries are often set by grants from the federal funding agencies), but some of these other things, like relocation expenses, are.  Talk to your current advisor or other postdocs to find out what’s typically negotiable in your field.  It usually doesn’t hurt to ask if you are reasonable about it.

So that’s it.  I hope the above points are useful to others out there, but if you disagree with something or have other points to add, please post a comment!

Applying for postdoctoral positions in the sciences (part I)

Having recently gone through the postdoc application process along with some of my peers, I thought it might be useful to summarize some of the things I learned.  But first one major caveat: the application process varies considerably across disciplines, even across subfields of the same discipline.  Just within physics, the process is fairly different for particle physicists versus condensed matter physicists versus biophysicists.  (NB: my area is theoretical and computational biophysics.)  Thus the universality of any one person’s experiences may be fairly limited, so please bear that in mind with everything I say!  So here goes…

  • Start early.  In some fields there is a well-defined application season (e.g., starting in the fall and concluding in January) and in others applications are accepted all year, but starting early is important in either case: you want to have the longest possible window to find opportunities.  In general, I think you should start looking about one year before you intend to graduate and start the new position — so start looking now if you will graduate in the spring of 2015.
  • Cast a wide net.  As you make a list of groups, fellowship programs, etc. you’re interested in, be as broad as possible.  Ask your advisor, other faculty, current postdocs, and other students for suggestions; there may be lots of interesting opportunities out there that you haven’t heard of.  You want to have as many options as possible.  For one thing, unlike undergrad or grad school applications, there’s usually little cost in applying to a huge number of these things (no fees and many have identical application requirements).  But besides that, many of these opportunities are very competitive and also subject to a good deal of luck.  Sometimes your dream group just isn’t hiring the year you’re looking for a job, or you just happen to apply when they are changing directions or when a rising superstar applies as well.  So your top few choices may become unavailable for lots of reasons, and you want to be prepared for that.
  • Apply for competitive fellowships.  Besides postdoc positions in individual research groups, many fields have fellowships for postdocs.  Some are federally funded (e.g., NSF or NIH), others are funded by private organizations, and others are specific to an institution.  The Graduate School-New Brunswick’s GradFund program has lots of resources on these, so check out their website and appointment offerings.  Fellowships tend to be extremely competitive, but you should apply for as many as you can anyway (remember the previous point?).  Many require the same materials you’d submit for any other postdoc application, so they require little additional effort.  Even if you don’t get a fellowship, applying to them can still have benefits.  Writing research proposals is an important skill, and the more practice you get, the better.  Maybe you’ll at least interview for one or two, providing another chance to meet people and practice interview skills.  Or maybe they’ll get your foot in the door for another opportunity.  Something like this actually happened to me: I applied for a fellowship that I ultimately didn’t get, but the process got my foot in the door with the group that sponsored my application and enabled me to receive a separate offer from them.
  • Write a research statement, but first figure out how it will be used.  Most applications ask for a “research statement” without specifying what this should include or how it will be used.  Since this may vary across disciplines and types of postdoc positions, I recommend trying to figure out the conventions for your field so you prepare your statement accordingly.  For example, one field I know consists of two subfields, and faculty merely use the research statement to determine which of those subfields you’re in.  So in this case the details of the statement don’t matter much and therefore aren’t worth a huge amount of your effort.  This was generally my experience as well — I doubt anyone read my statement in much detail beyond skimming the general topics I listed.  (Note: this is in contrast to a research proposal for a grant or fellowship, which likely WILL be scrutinized carefully!)
  • Have a decent CV and website.  Besides your research statement, most applications will require a CV.  I won’t cover how to make a CV here, but spend some time making it organized and easy-to-read if you haven’t already.  I also recommend setting up at least a basic website if possible.  I made a personal website early in grad school, but for the most part it hasn’t served much purpose.  So I was a little surprised to realize people were looking at it when I applied for postdoc jobs.  I’m sure they didn’t peruse it in detail, but they at least saw my picture and probably glanced at my papers, research interests, and teaching activities.  This probably doesn’t make a big difference, but it’s another data point to confirm your legitimacy, especially for a professor drowning in dubious applications.  So if you already have a website, make sure it’s up-to-date and be aware of what you put on it; if you don’t have a website, consider setting up a basic one.  It doesn’t need to be fancy, just a place to post contact information, your CV, maybe a photograph.  If you don’t know HTML, web services like WordPress offer easy-to-use templates, and even simple composers like iWeb or Microsoft Word will get the job done.  Get it linked somewhere on your department’s or advisor’s page to make it easier to find.

I hope these thoughts are useful to some of you — next week I will post part II.  In the meantime, feel free to share your experiences and ideas in the comments!

“Sabbaticals” for graduate students

Dynamic Ecology is a fantastic blog (written by a small group of contributors) on various topics in academic research and careers, especially in evolution and ecology.  They just featured a provocative new post advancing the idea of taking a “graduate student sabbatical” — when a grad student spends a long period of time somewhere outside of his/her home institution — to achieve research goals (e.g., forming a new collaboration, facilitating field work) or to accommodate family needs (e.g., a significant other with a job elsewhere).  Usually we only think of sabbaticals for faculty members, but grad students often do similar things all the time, even if we typically don’t call them sabbaticals.  It’s a fascinating angle, I recommend checking it out!

Making the most of scientific conferences

Conference experiences have been explored a few times on this blog before, but given the apparent diversity of conference formats across disciplines, I think another perspective might be valuable.  The topic is particularly on my mind since I recently attended the American Physical Society (the major professional society for physicists) March Meeting, which took place in Denver this year.  March Meeting is by no means the only important conference in the physical sciences, but it is probably the biggest — almost 10,000 people, from undergrads to Nobel Prize winners, attend from all around the world.

I’ve been to March Meeting three times now, plus a few smaller conferences.  Now that I’m nearing the end of graduate school, it thus seems like a sensible time to reflect on how to make the most of these trips.  Optimizing your conference experience is important, since conferences are usually a substantial investment of your time, energy, and money (maybe your advisor’s money, but still…), and they can be key opportunities to advance your career.  So here are some thoughts on the matter I’ve acquired over the past few years:

  • Don’t try to attend everything.  This was probably my biggest mistake at earlier conferences, and I think it’s a common one to make.  It’s so easy to have eyes bigger than your brain when you look at the schedule of talks.  I would try to attend everything the first day or two, and then I would inevitably burn out and end up missing or sleepwalking through some more important events later on.  Try to prioritize the absolute most important things on the schedule before the trip, and make a reasonable plan of how much you can actually do.  Be conservative with your judgment.  It’s better to sleep late and attend only a few talks that you really pay attention to, rather than to wake up early and attend everything but be so tired that you don’t learn anything.  So how should your prioritize events?  Well…..
  • Meeting people is the most important thing.  Specifically, it is more important than any talk.  Talks definitely can be useful — they put your finger on the pulse of cutting-edge research and can expand your breadth in unexpected ways — but there are still alternative ways of learning about research.  You can always read someone’s papers if you really want to know about their work.  But there is no substitute for interacting with people face-to-face at a conference.  This is how you form new collaborations and meet people who may someday offer you a job.  So when budgeting your time and energy, opportunities to meet people should always come first.  Skip the talks and just go to the reception afterward if you have to.  Now that I’ve stressed its importance, how do you actually go about meeting people?
  • Be a little shameless.  It’s hard to summon the courage to ask questions during a talk or introduce yourself to someone new, especially when they are much more senior and your questions and ideas seem naive.  But you have to be a little shameless and do it anyway.  The particle physicist Tommaso Dorigo has some nice ideas on his blog about how to come up with questions for these occasions.  The point is that even if your questions are a bit vacuous, or your attempt to introduce yourself and shake hands with that famous person feels awkward and forced, the mere process of getting practice doing it will be beneficial.  By the time your questions and ideas are more substantial, you’ll already feel quite comfortable speaking up.  Despite science’s reputation as being the domain of introverts and nerds, in my experience the scientific community rewards assertive, outgoing social behavior, people who are aggressive about seeking knowledge and maybe even a little self-promoting.  Being “that person who keeps asking questions” will make you stand out and gain respect as a passionate seeker of knowledge.  I played such a role at a few events in the past (ones with small audiences, which made this a lot easier), and several people even told me afterward that they noticed me because of all my questions.  Hopefully I wasn’t too annoying, but at least they noticed me!  But besides meeting new people from scratch, a much easier route is to…..
  • Use your existing connections to make new ones.  It’s always easier to meet people through people you already know.  So if you already know one or two people at a conference, spend enough time with them to meet some of the other people they know.  Getting to know grad students or postdocs at other institutions is a great strategy: as a grad student yourself, it’s usually not too hard to meet and get quality time with other young people (compared to, say, faculty), and once you get to know each other, they should be more than happy to introduce you to their friends at their own institution or other people they happen to know.  And you can do the same for them.  Finally, once you’ve met some new people…..
  • Follow up with the new people that you meet.  This can be tricky, but it’s important if you want those new connections to last.  I have been able to invite a few people I met at previous events to give seminars for our group here at Rutgers, which obviously helped a good deal in solidifying those relationships.  But that’s not always possible.  Sometimes it’s reasonable to send a follow up e-mail to someone you just met.  For example, you might talk to someone about a paper they wrote, and after you go home and read it, you could easily send them an e-mail with a generic pleasantry (“It was nice to meet you at that conference…”) followed by a question or two about the paper.  There’s no need to be sycophantic, but if you are honestly interested in their work, it shouldn’t be hard to come up with a few genuine questions.  A short e-mail exchange like this will go a long way in preventing you both from forgetting each other.  In the worst case, try to track down your new contacts at the next conference, even if it’s a year or two in the future.  They’ll probably be flattered that you remembered them and reached out.  If your memory for names and faces isn’t acute, find other ways of keeping track of the people you meet: for example, you can ask for business cards (not common in science, but apparently common in other disciplines) or keep a list of professional contacts.

I’m sure five years from now my views on conference-going will have evolved even further, but the foregoing points have at least served me well as I finish up my Ph.D. and prepare for the next stage.  So I hope someone else will find them useful as well.  In any case, I’m sure these issues probably vary widely across disciplines (and even within a discipline, too, depending on the conference), so different perspectives are welcome in the comments!

From the computer screen to the lab bench: A physicist learns to do wet-lab biology

As Kenneth described in a recent post, the Center for Integrated Proteomics Research and the BioMaPS Institute for Quantitative Biology at Rutgers recently held a two-week “boot camp” program to cover a range of basic topics in molecular biology and biophysics.  The program was intended to serve the increasingly diverse community of scientists — with backgrounds ranging from physics and chemistry to computer science and mathematics — working in quantitative biology.

As a physics graduate student who works in the BioMaPS Institute, I was definitely in the target demographic.  In my undergraduate days I was mainly interested in particle physics and cosmology, so my coursework focused entirely on physics and mathematics.  I haven’t taken a biology or chemistry course since high school.  While I’ve certainly picked up a great deal of the necessary biology throughout my graduate research in biophysics, I could still use more breadth.

But I was primarily interested in gaining a very specific kind of breadth from the boot camp.  Besides having a background in physics, I am also a theorist by training, and I’d never had any experience doing “wet-lab” biology experiments.  (In physics, there is a very clear divide between theorists and experimentalists.)  But recently I’ve become interested in gaining some experience with wet-lab biology, both because it’s helpful for collaborating with experimentalists and understanding experimental papers, but also because there is a serious possibility I will pursue a combination of theoretical and experimental work next year as a postdoc.  Luckily, the boot camp included a week-long experimental lab for complete beginners like me, so it seemed like the perfect opportunity to try it out.

The best thing about having zero experience with something is that you can learn a whole lot really quickly.  So even the most basic, mundane aspects of doing the lab were new and exciting for me, things I had heard about in talks or read in papers but never really understood.  So this is how you pipette…and “streak a plate”…and purify proteins…and run a gel…and so on.  Here’s some photographic evidence (credit to Gail Ferstandig Arnold):

As someone who has worked only on the other side of research until now, it has been really eye-opening to have concrete experience doing experiments and generating data that previously existed only as abstractions in my theorist’s mind.  While I recognize that a week’s worth of exposure isn’t enough for me to jump right into doing all my own experiments as a postdoc — undoubtedly I’ll have to relearn all the stuff from last week again later — getting that first experience definitely gives me confidence for the future.

Communicating science: the elevator speech

In a previous post, I described my experience at a workshop (organized by the Rutgers Graduate School-New Brunswick) on communicating science.  I described the importance of preparing descriptions of your work for a spectrum of likely audiences – having at least some idea of what aspects of your work to emphasize to different audiences and what language or ideas to use are critical.  However, in addition to these more customized versions, having a more generic but highly-polished description of your research that you can recite from memory at any time is probably worth having.  This is often known as the “elevator speech,” since it’s supposed to be something simple and short enough that you can say it during the time you’d spend with a stranger in an elevator.

I’ve had a murky version of this for a while, but it was largely a vague set of examples and analogies I liked to use when describing my research to a friend or family member rather than a well-crafted summary.  But the workshop motivated me to finally develop a better version, so here is my latest attempt:

Every cell in your body contains thousands of different kinds of molecules, stuffed into a very small space and interacting with each other in complex ways.  How does this mess of molecules ultimately do all things that cells do, such as making new cells, extracting energy from food, and transporting nutrients?  And how did the precise interactions of all these molecules develop over millions of years of evolution?  This knowledge is important both for treating human diseases in which these cellular functions go wrong (e.g., runaway cancer cell growth), as well as engineering microorganisms to perform useful jobs, such as synthesizing biofuels with bacteria or making better beer with yeast.  My research uses mathematical models and computational techniques to understand how natural selection changes these molecules and their interactions over time.  We want to use this both to understand how organisms naturally evolved in the past and to predict how they might evolve in the future.

Communicating science: simple language for complex ideas

For those who don’t know, the Rutgers graduate school (through Project AGER) regularly offers a variety of outstanding workshops on professional development for grad students.  I recently attended one on science communication.  The workshop was run by Sangya Varma, of the Rutgers Professional Science Master’s Program and an alumna of the Alan Alda Center for Communicating Science at Stony Brook University.  (In his post-M*A*S*H career, Alan Alda hosted Scientific American Frontiers on PBS for many years and has been a vocal advocate for popularizing science.)  The center at Stony Brook offers multiple courses, a master’s program, and various workshops to train scientists to better communicate their work with different audiences.  It’s a fascinating and one-of-a-kind place, and I for one would love to take part in some of their activities.

The two-hour workshops at Rutgers provide a small sample of what the center at Stony Brook offers.  After highlighting the basic motivation for scientists to cultivate communication skills and some general principles of how to convey complex ideas in simple ways, we embarked on exercises of “translating” our own research into accessible language.  We also chose from a list of specific audiences (e.g., a family member, a group of investors, a newspaper reporter, etc.) and spontaneously tried to present our research to that audience.

This last activity really hit home for me, since a few months ago I participated in an interview with members of my group about our research for The Daily Targum.  Neither we nor the reporter had much experience with this, and while the resulting article was a nice plug, I was rather dissatisfied with it.  We ended up saying very little in the interview about our specific research activities, instead being sidetracked on general issues about the state of the field.  I also realized how terrible the spontaneous things we say aloud look when put into print.  I learned that one really has to prepare for these things: you have little control over what the reporter will pick to include in the final article from whatever you said in the interview, so you have to give them a very polished set of statements (pretty much at the level of sound bites, which is what they will end up using) that you won’t regret having in print.  Speaking off the cuff makes it too easy to say something careless, incoherent, or just plain silly.

This previous experience and the workshop (plus all those times I felt dissatisfied after trying to explain my work to friends and family) have inspired me to take a more deliberate approach in the future for communicating my science.  I’m starting with a list of audiences that I may likely interact with, based on my research and career interests:

  • Family members and friends
  • Basic life scientists outside of my specific subfield (e.g., molecular biologists)
  • Physicists outside of my subfield (e.g., condensed matter physicists)
  • Biomedical scientists (e.g., cancer biologists)
  • Biotechnology scientists and entrepreneurs
  • Science news media (e.g., Scientific American)
  • Mainstream news media (e.g., NY Times, Rutgers Today)
  • Program officers and review panels at funding agencies (e.g., NIH, NSF, private foundations)

My goal is to prepare short descriptions of my work customized for each of these audiences.  Most of us have at least partially done this implicitly — say, by writing applications to different funding sources or concocting one spiel about your work for your parents and another spiel for your grad student friends.  But I think a more systematic approach is a good future goal.  Even a list of important points or key words to emphasize for each audience is probably helpful; for most of us, we will definitely emphasize slightly different points or use different words for distinct audiences.  For me, I would likely emphasize the “coolness” and basic science relevance of my work when speaking to my friends or peers in science (especially from physics), while to an audience of biotech people I would definitely emphasize future potential applications.

What lab reports can learn from literary analysis

The lab report is a staple of introductory science classes, so anyone who’s taken such a class knows how it goes. There’s a hypothesis, then an experimental procedure, then some data, then a discussion of whether the data agrees with the hypothesis. While the spirit of the assignment is good — emphasizing the importance of empirical verification through an experiment — it perpetuates some key misunderstandings about how real science is done.

As many commentators have previously complained, standard labs teach students that doing science means following a recipe (e.g., the instructions from your lab book), and there is a “right” way to do it and a “wrong” way to do it. (Of course, the “right” way results in data that agrees with the hypothesis.) Practicing scientists know that actual science looks nothing like this. You rarely start with a clearly-defined hypothesis and straightforward experiment to test it. Instead you usually just have some vague idea you want to investigate, and then you do some calculations, perform some experiments, whatever you can think of, but with no guarantee they will work or solve your problem. And often you end up addressing a problem different from the original one you were trying to solve (see my post about this here).

But I contend the lab report fails to teach another important aspect of science: how to craft a persuasive, evidence-based narrative. Real scientists almost never write anything that looks like a lab report. A lab report is, well, just a report: rigid, sterile, lacking any point of view. Reports are what police officers write after they investigate a crime. Scientists write papers for scholarly journals. And scientific papers, in my opinion, are much more like the literary analyses I used to write for humanities classes. They’re persuasive. They have a point of view. You start off with a thesis, which can be pretty specific and quantitative (“My model in equation 1 describes the data well”) or broad and qualitative (“Protein folding stability is the main determinant of protein evolution”). But just like in literary analysis, you’re advancing a point of view, and your job is to convince the reader that it’s valid. To support the thesis you build a narrative based on evidence — in literary analysis, this may be quotations from the work being analyzed or historical facts about the author, while in science the evidence is experimental data and calculations. One professor I had in college described scientists as “lawyers for the natural world.” Your paper describes your case. You are trying to make a persuasive case about some phenomenon in nature, convincing the readers (the jury) that your thesis is correct.

The cold, rigid nature of the lab report pretty much kills this aspect of doing science. To students the lab report mainly serves as proof that they did the experiment “correctly,” and any discussion of the data is perfunctory and merely reiterates what they think is obvious, that the data agrees with the hypothesis. We need to break free from the rigid structure of the lab report and allow students to see their write-ups as opportunities to craft convincing narratives in support of a (scientific) point of view, supported by evidence. We should select topics that allow students to form a non-obvious point of view that must be carefully justified with data and argument, rather than giving them experiments where the outcome is obvious and the data is self-evident. Not only would this teach a much richer and more accurate version of science, but it reveals a major place of harmony for the sciences and humanities: how to use evidence and logical argument to support an idea through writing.

Teaching physics with social media

It shouldn’t be surprising to see social media seeping its way into classrooms these days, given its growing diversity and ubiquity.  I had the chance to try social media for a class I team-taught last spring, Physics 106 (Concepts of Physics for Humanities and Social Science Students, also known as “Physics for Poets”).  Previous incarnations of the course have essentially been watered-down versions of the introductory physics courses for pre-med and engineering students.  Along with three other graduate students, this year we completely redesigned the course to focus less on blocks sliding on mysteriously frictionless surfaces, and more on modern, relevant topics like cosmology, energy sustainability, and superconductivity.

We experimented with making social media a major part of the course.  Although this runs the risk of being a mere gimmick, we were committed to social media applications that were really in the best interests of the class.  Since the course is intended for students not pursuing scientific careers, one of our main goals was to stoke the students’ interest and develop their familiarity with popular science media, which is how the students will likely access science for the rest of their lives.  Popular science, like so much media these days, has a major presence on social media, especially Twitter and blogs.  To that end, we incorporated Twitter and blogging into the course.  We created a Twitter feed for the class (@RUPhys106), and several times a week we tweeted links to articles, videos, and websites with cool science content, most of which was directly related to the current course material.  For example, we were able to share this interactive NY Times feature on the hunt for the Higgs boson when we discussed particle physics.  When we talked about protein folding, we tweeted this beautiful blog with art inspired by protein structures.  Out of the approximately 100 students in the class, we accumulated a few dozen followers; we also embedded the feed into our Sakai homepage, which meant students who didn’t use Twitter or didn’t follow us still would see our tweets.

We also had the students write two blogs.  The topics were related to material we covered in class, but that required them to pursue further reading and develop their own take.  The students first posted drafts of these blogs to Sakai through the built-in blogging tool, and then each student had to review two of their peers’ blogs and leave comments.  Using this feedback and additional feedback from the instructors, the students revised their blogs into final drafts.  We were very impressed with the quality of many final blogs; several had the potential to be posted publicly.

Obviously, our use of both Twitter and blogging had direct benefits within the course — the articles and videos linked in our tweets provided content enrichment beyond the lectures, and the blogs required the students to learn to express scientific ideas in their own words.  But beyond these immediate benefits, our hope is that many students have come away with more familiarity and excitement about the outstanding popular science media out there: all the great Twitter feeds, blogs, websites, YouTube channels, etc.  Regardless of whether any of our students remember what wave-particle duality is 10 years from now, if they keep clicking on links about quantum mechanics as much as they do for links on the Kardashians or the world’s 12 cutest animals, our course will have been a success.

Fight for your right — no, your privilege — to do science

At the American Physical Society March Meeting a few weeks ago — the biggest confluence of physicists in the world, with over 9000 in attendance — there was a session titled “American Science and America’s Future.”  Now, who could miss a session with a grandiose name like that?  Well, it seems that a lot of people could, since the cavernous ballroom they reserved for it was less than 10% full.  To be fair, I attended a similar session last year, which featured much better attendance.  Having a Nobel Prize-winner on the panel probably helped.  But this year’s disinterest disturbed me, as did the small number of people who signed the periodic form letters APS prepares for members to send to Congress.

The fact of the matter is that most of us do science at the pleasure of the public.  We as a society have decided that scientific research is something we value — ostensibly because of its future economic dividends but also because, frankly, it’s one of the things that makes a civilization great — and since it’s something the market won’t carry out on its own, we pay for it with taxes.  So our ability to continue the scientific research enterprise that has made the United States the most powerful economic, cultural, and intellectual force in the world rests squarely on taxpayers, and more importantly, their political representatives, continuing to value what we do.  If they don’t, our privilege could be taken away.

My fear is that many scientists view this support as an entitlement, a right to follow their scientific curiosity wherever it takes them on taxpayer expense.  This hubris is not only selfish, but dangerous.  Without proper advocacy and education, the public and the political leadership are at serious risk of losing sight of science’s value to society.  There is already frequent grumbling about cuts to federal funding agencies, widespread ignorance of scientific issues affecting society like climate change and healthcare, and the growing weaknesses in science education in the U.S.  While the NSF and NIH aren’t going to shut down anytime soon, it’s very possible that science funding could face gradual cutbacks or at least radically slowed growth, especially in the face of competing funding priorities.  If and when this happens, scientists shouldn’t blame the ignorant public or politicians — they will have to blame themselves, because that ignorance is our fault.

So the time is now for scientists to take action.  Get in touch with your political representatives, both local and federal.  Write letters to the newspaper.  Be active in your community, so your neighbors can be in that small minority of folks who know a real, live scientist.  Get involved in public outreach.  But whatever you do, don’t take your research support for granted.  Let’s get the science that we all pay for with our taxes into the public consciousness.

The Nurturing Paradigm of Scientific Training

Uri Alon, a biophysicist at the Weizmann Institute in Israel, likes to tell a story about when he first became a faculty member.  Already an accomplished researcher, he stepped into his empty new lab and immediately felt overwhelmed.  Despite all the training he’d received about how to do science, there was so much more to being a scientist that he was completely unprepared for: setting up a laboratory, recruiting students and postdocs, developing good projects for students and postdocs, managing a large team, mentoring young people for the next stages of their careers, and so on.  As critical as these skills are to being successful, there is very little emphasis on developing these skills early in one’s career.

Indeed, there seems to be little respect in the scientific community for the importance of these “soft skills,” at least in comparison to the technical skills required to do the research itself.  As a result of his personal experiences, Uri Alon has led a small crusade toward greater emphasis of the human aspect of doing science.  On his website he’s compiled a growing set of resources called “Materials for Nurturing Scientists,” including articles, videos, and songs, authored by both himself and others.  Topics include how to choose a scientific problem, how to give a good talk, how to build a motivated research group, how to achieve work-life balance, and more.  He also has developed support groups for young scientists at his institution and has advised other institutions how to do the same.  His title evokes a compelling vision: one in which one’s goal as an advisor to students and postdocs goes far beyond merely supervising their research.  The “nurturing paradigm” entails holistically developing young people in every aspect of becoming a professional scientist.  Having heard Uri Alon speak (and sing songs) about these issues multiple times in person, his vision is certainly an inspiration to me.

The Hidden Virtues of Wasting Time

For the benefit of the incoming graduate students, my department in college used to take surveys of everyone about what they would do if they were starting graduate school over again.  (They called this “Starting Over,” and it was such a fantastic idea that I shamelessly ripped off the idea when I came here.  Here are our results.)  As interesting as all the comments were, I was always most fascinated by the clear difference between the current student responses and the faculty responses.  The current students tended to dispense wisdom about academics, research, and the minutiae of navigating a Ph.D.  A lot of “study hard for your quals” and “start writing your dissertation early.”  The faculty, though, rarely mentioned such details.  Rather, they focused on…..well, how to stay human.  They tended to submit entreaties to go outside and exercise, to make time for family and friends, to stay healthy, and so on.  Not exactly what we’d expect from a profession that is notorious for its workaholism (which also seems to have led to a serious case of caffeine addiction).

So what’s going on?  These faculty members are presumably the successful ones, so an interpretation of their advice is that they’re (1) expressing regret they didn’t live better when they were younger, or (2) telling us the secret of their success.  The aforementioned study on the working habits of scientists might make us doubt the latter interpretation.  So if you’re looking for yet another reason to feel guilty for not working all the time, well, here you go.  But I think this oversimplifies the situation.  First, one’s optimal work-life balance is not static over time — one’s needs as a graduate student are different from those as a young professional which are different from a mid-career person.  So what might seem workaholic now maybe will be more comfortable in 15 years, or vice versa.  Second, work-life balance has a great deal of person-to-person heterogeneity.  A lifestyle that is balanced for one person may be too overwhelming for another, and too freewheeling for a third.  The effects of this balance on one’s actual productivity are also not as simple as we might think.  I know some folks who seem to work almost all the time, and yet they don’t seem to accomplish a whole lot.  On the other hand, I know someone who has more fun than almost everyone I know, and yet he’s reached a level of professional success most of us can only dream of.  (I’m still talking about scientists, by the way!)

Perhaps the takeaway, then, is not only to take seriously the need for balance, but to consider seriously one’s very individualized needs for it, rather than letting it be determined by cultural or social norms.  The work-life balance you strike should be the result of your deliberate choice, and not the inevitable consequence of external pressures or other choices you make.  If faculty wisdom is to be believed, then it sounds like you won’t regret it.

Breaking through the Jargon Barrier

While recently reading an article in an education journal [1], the word “frame” kept jumping out at me.  The author, a sociologist, kept using this normally unremarkable word in a way that I found unusual and confusing.  Soon, though, I realized that “frame” was probably a piece of jargon with a specific meaning within sociology, distinct from its everyday use in English.

The author likely failed to clearly explain this usage (he parenthetically defines it later in the article, unfortunately not immediately after the first instance) because he was so accustomed to speaking sociology’s language of jargon that he forgot the double meaning of this word: its standard English usage, and its sociology usage.  Certainly this is an easy mistake to make for any scholar, but it poses a barrier to effective communication of ideas to a larger audience.

I think there are generally two classes of jargon which (in the spirit of creating even more jargon) I will define as class I and class II.  Class I consists of words that are unique to a particular field of knowledge, with no meaning in standard English.  We have lots of excellent examples of these in physics: “fermion,” “quasar,” or more infamously, “boojum” [2].  While these terms tend to be the scariest for a non-technical audience, in some sense they are also safer from a communication standpoint: “fermion” has no meaning outside of physics, so while lots of folks won’t know what you’re talking about if you say it, they will never confuse it with something else.

Class II is sneakier.  It consists of words that DO have a common, everyday meaning, but also have a very specific technical meaning within a field, like the aforementioned example of “frame.”  Ref. [3], which discusses the challenge of communicating climate science to the public, provides several fascinating examples of such words.  The most notorious of these words is probably “theory.”  To a scientist, theories are the most established and complete scientific ideas, typically referring to whole frameworks for understanding a wide range of phenomena that have been rigorously validated by experiments and observations over decades.  Good examples include Newton’s law of gravity, quantum mechanics, and evolution.  To the layperson, however, a theory is what a scientist would call a “hypothesis” or “claim”: an educated guess that hasn’t been verified or fully understood yet (e.g., “conspiracy theory”).  Obviously, you can see why biologists cringe every time someone derides Darwinian evolution as a mere “theory”!

So while we tend to focus most of our attention on class I jargon words when communicating to a wider audience, we should pay greater attention to class II words.  They have much more potential to mislead.  This was demonstrated especially in the recent “Climategate” ordeal, in which e-mails of climate science researchers were made public.  One point of contention for climate science deniers was the scientists’ use of the term “trick” in analyzing data.  Most scientists recognize this usage as referring to a legitimate but clever method for solving a technical problem (e.g., “I solved the equation using Fourier’s trick”).  But in ordinary English, “trick” usually refers to an intentional act of deception, which is obviously what climate science deniers were hoping to find in the e-mails.  Awareness of these class II terms in our respective disciplines, and an alert eye for them while reading about other disciplines, would serve us all well.

[1]  Wilson WJ.  (2011)  “Being Poor, Black, and American: The Impact of Political, Economic, and Cultural Forces.”  American Educator, Spring: 10.
[2]  Mermin ND.  (1981)  “E Pluribus Boojum: the physicist as neologist.”  Phys. Today 34: 46.
[3]  Somerville RCJ, Hassol SJ.  (2011)  “Communicating the science of climate change.”  Phys. Today 64: 48.

Randomly Walking through Research

From reading papers, it’s easy to gain the following picture of what the research process looks like: someone starts at point A, a known point in the space of knowledge, then directly proceeds through various arguments and data to one’s conclusion at the previously unknown point B.  However, thinking that research actually works this way based on what you see in a paper is like thinking that Michael Jordan just awoke one day and suddenly starting dunking from the free throw line.

No, MJ almost certainly traveled a long road to get that much air.  The same goes for research.  The real research process more resembles the famous physics concept of a “random walk” (or more colorfully, the “drunkard’s walk”).  In a random walk, some process is imagined as an object, perhaps an inebriated human, taking a step in a random direction at regular time intervals [1].  This idea is used to model everything from chemical reactions to stock markets.

The random walk provides an interesting visualization of the research process as well.  Uri Alon, a scientist at the Weizmann Institute in Israel (whose outstanding set of resources for “Nurturing Scientists” will be a topic for future posts), has described the process as the following [2].  You indeed start out at A, headed for B.  (See figure below.)  But instead of a nice straight route, you embark on an irregular trajectory with many detours, barriers, and delays.  Often you are eventually forced to abandon B altogether: B was already discovered by some Russian guys in the 1970s, or maybe it’s impossible, or perhaps it just can’t be reached if you hope to graduate within the current decade.Image

At this point your random walk enters a limbo state that Uri Alon calls “the cloud”: you know you can’t go to B anymore, but you don’t know where else to go.  Being stuck in the cloud is probably the most difficult part of doing research.  But the key is recognizing this is a natural and inevitable part of the process.  If you persevere, you will leave the cloud by eventually finding a new place to go: point C.  In fact, often C is much more interesting than B would have been anyway — the unexpected almost always is.  Of course, sometimes C also fails to work out, too, in which case you redirect to D, E, F, etc.  (Hopefully you don’t run out of letters!)  The point is that research is less like a direct path from A to B and more like a random walk with an unknown trajectory and an unknown destination.  But after all, it is this journey into the unknown that makes research so exciting and so important.

[1]  Mlodinow L.  (2008)  The Drunkard’s Walk: How Randomness Rules Our Lives.  Pantheon, New York.
[2]  Alon U.  (2009)  “How To Choose a Good Scientific Problem.”  Molecular Cell 35: 726-728