This post is part two of a three-part series on applying to PhD programs in computational biology. If you haven’t already, I’d recommend reading the previous post, “Three Reasons You Shouldn’t Apply to Grad School,” for all the caveats this post lacks. If you’re here, it means that you’ve thought through the negative aspects of doing a PhD and decided that you’re willing to deal with them. Now that they are out of the way, let’s talk about why grad school can be great.
As in the previous post, this post is not comprehensive and is based on reasons I decided to go to grad school. Your mileage may vary.
The number one reason to pursue a PhD is the freedom it affords you. Every morning of grad school you get to wake up and decide what you’re going to work on that day.
In industry, the work you do is determined by what is most valuable to your employer. In academia, your work is guided by your lab’s focus, but you are free to start side projects that may end up becoming your main project. You even have the ability to apply for a fellowship to get even more freedom to work on the topics that interest you.
There is no fellowship equivalent that I know of in an industry job1. If you don’t like your team’s focus, you either have to learn to like it, change teams, or find a different employer. Even if you were to create a startup, you would still be beholden to the business’s growth. Startups may be risky in terms of execution, but they typically try to minimize research risk.
As a grad student, you get a tremendous amount of independence. You may end up working with collaborators, but it is unlikely that anyone will be telling you what to do or how to do it. Through the course of a PhD you become an expert in your field, and in doing so you will gain the ability to chart your own course.
This independence isn’t an unqualified advantage though. Different people prefer different levels of independence. However, if getting to decide how you’re going to solve hard problems sounds exciting to you, then you’re probably well-suited to the academic path.
Few Hard Time Constraints
Once you pass your candidacy exams and finish your coursework, you’ll find that you have very few deadlines on your horizon. You’ll have thesis committee meetings once or twice a year and occasional conference submission deadlines, but for the most part your time is your own. If you’re a night owl, you can show up to work at noon if you want. If you want to take a day off, you just do it. Certainly this freedom is dependent on lab policy (and any time-sensitive experiments you might be running), but mostly your schedule says “I need to get these ten things done in the next few months” rather than “I need to get these two things done by Friday.”
One of the things that always bothered me about science classes is that you don’t actually do any science. Lab classes often look something like “your professor explains a concept -> you run an experiment related to that concept -> you ‘discover’ that gravity is a thing that exists.” Lecture classes are worse. A typical lecture class consists of a professor explaining a concept, time passing, and you writing the concept down on a sheet of paper titled “Exam”. Science classes aren’t designed to handle questions like “how do you know that’s true” or “have other people replicated that finding?” Rather, the goal is to convey information that other scientists have already discovered.
On the other hand, as a PhD student you get to find your own answers. Instead of treating everything you hear as true, you read journal articles and weigh the evidence of their claims in your head based on their internal and external validity. Instead of being told “run this experiment to confirm this concept,” you design your own experiments to learn the truth for yourself. Through this process, you get to use your skills, your intuition, and the accumulated knowledge of the scientific community to find answers to some of the most important problems in the world.
Finding these answers is difficult, of course. But if you’re drawn to Type 2 Fun, a PhD can be extremely rewarding. Doing research gives you the opportunity to experience and overcome failure daily. Very few experiments will work on the first try, if at all. However, you eventually learn enough, get lucky enough, and build a strong enough intuition that your experiments are successful. This “try, fail, get better, repeat” loop is not something you can find much in day-to-day life2. As a grad student, though, that dynamic is the core of your work.
If you’ve read the previous post, then you know there are a number of reasons not to do a PhD. The people who have decided to go the academic route are also aware of those drawbacks3. Your coworkers in academia will be individuals who decided that they value freedom, independence, and intellectual challenges enough that all the negatives are worth handling. That’s not to say that you’ll like everyone you meet in grad school, but academia definitely selects for interesting people.
I recently talked about how a PhD can be isolating because you spend your days thinking about things most people have never heard of. My labmate Alex read the section and replied, “This sounds familiar, but we have each other to talk about VAEs to :)” She brings up an excellent point. While your thoughts will deviate further and further away from the average person’s, the people you work with will be some of the only ones in the world thinking about the same thing. Part of the cost of your ticket into PhD research may be isolation, but that ticket gains you admission into a group of people who care about the same questions as you.
If you’re going to do a PhD, you should go into that decision knowing what lies ahead of you. Grad school is hard, and there are many good and bad aspects about it. Hopefully the pros and cons are clearer to you now, so that you can make a more informed decision. If you decide that you do want to go to grad school, I’m putting out a post in a week with information about applying to computational biology programs.
Chris Albon did an episode on Partially Derivative about whether you should apply to a PhD. I found it helpful when I was considering applying to PhDs, so you might like it too.
“The answer is basically… yes, but let’s talk about it” - “Should you do a PhD” from OMGenomics
“Should you get a PhD? The answer to that depends on two questions? Do you need one? Do you want one?” - “Advice to Prospective Grad Students”
“Top N Reasons To Do a Ph.D. or Post-Doc in Bioinformatics/Computational Biology” This post is written to convince people with a biology background to transition to bioinformatics/computational biology, so it may be more or less applicable to you.
There are industry labs that exist somewhere on the continuum between academic lab and industry position, but I’m talking about non-research industry jobs. ↩
If they weren’t before they applied, they certainly are now. ↩