This was one of the best written posts on the forum. It’s clearly motivated, expresses the issues, the context and your uncertainty and confidence well.
I think you have good answers already.
Here’s some other considerations (that are mostly general):
Fit is important. How well you feel in the lab matters a lot. If you feel like the people are overbearing or if you have to fight, that’s bad. This can be hard to figure out and worth thinking about. There is a honeymoon period, and small things can play a big role 1-2 years later. Many people become miserable.
(For an MA, it might matter less) but the actual placement record of the PI is pretty important. This is not just for the usual reason of academic status, but I think it’s gives an important sense of how good the work/lab is.
Especially for an MA, the PI actually might not be very hands on or determine your experience (especially for high status PIs). Often it’s actually the post-docs (lab techs sometimes) that are extremely important for students and can make you successful, or make your life a living hell.
Note that people leave and a small trap is joining a lab where the talent (post docs) take a new position, which change everything for you.
Asking around for reputation is important.
It’s unusual and I’m unsure how likely it is, but it’s possible you might be able to swing some sort of EA mentorship or grant, which might make your second choice more interesting if you can point it toward pandemics.
Overall (maybe because of my bias described below), I generally distrust labs where you are fitted in a slot to solve a problem for a PI. I recommend a lab where you can gain general programming skills or have freedom to network and express your abilities.
More background:
I’m against/biased against a lot of “hard science” grad work, because (maybe outside of a small number of labs/PIs) I think you often just serve as cheap labor that doesn’t actually involve a lot intellectual activity (this is a distinct from the other problem a lot of research activity is winning games in academia).
This might be different and not apply to bioinformatics or something exposed to a lot of programming.
I’m against/biased against a lot of “hard science” grad work, because (maybe outside of a small number of labs/PIs) I think you often just serve as cheap labor that doesn’t actually involve a lot intellectual activity (this is a distinct from the other problem a lot of research activity is winning games in academia).
I’d offer a slightly different perspective on this point, as a hard science graduate student doing a lot of cheap labor with most of my intellectual activity done on the side.
When you do cheap “non-intellectual” work in a hard science lab, you learn:
A practical sense of how to design an experiment to answer an important question
An intuition for how to connect data in papers (and content in textbooks) to what the scientists actually did in the lab, and what the limitations, implications, and importance might be in light of that.
The skills to develop and troubleshoot new methodologies
How to collaborate and communicate with your supervisor, colleagues, tech support, other labs, core facilities, and authors of papers you’re building on
A sense of what bottlenecks hold things back in the lab, as fodder for future research directions as well as any possible future management role you may have
I have gotten a ton of value out of both rote lab techniques and the arduous process of troubleshooting methods for a novel experimental design.
Hi Charles, thank you for taking the time to write your reply, you’ve given me some really valuable advice. Those are some valuable general considerations to keep in mind regardless of which direction I go. I know that in the first lab, the third point is especially relevant as I was told that there is a postdoc that will be providing guidance for me throughout, so that’s something for me to be mindful of.
Your fourth point is something I’ll give a bit more thought as well, especially because I know the second lab recently did a project monitoring a population to see which pathogens were present, so there’s definitely scope for me to shape a project in that area.
I appreciate the final point too, that’s something to be careful of and I should try to get a better handle on how much freedom I’ll have before I commit. Hopefully, as you say, that’s not as generally true for bioinformatics-related projects but I can see there still being a risk of that.
This was one of the best written posts on the forum. It’s clearly motivated, expresses the issues, the context and your uncertainty and confidence well.
I think you have good answers already.
Here’s some other considerations (that are mostly general):
Fit is important. How well you feel in the lab matters a lot. If you feel like the people are overbearing or if you have to fight, that’s bad. This can be hard to figure out and worth thinking about. There is a honeymoon period, and small things can play a big role 1-2 years later. Many people become miserable.
(For an MA, it might matter less) but the actual placement record of the PI is pretty important. This is not just for the usual reason of academic status, but I think it’s gives an important sense of how good the work/lab is.
Especially for an MA, the PI actually might not be very hands on or determine your experience (especially for high status PIs). Often it’s actually the post-docs (lab techs sometimes) that are extremely important for students and can make you successful, or make your life a living hell.
Note that people leave and a small trap is joining a lab where the talent (post docs) take a new position, which change everything for you.
Asking around for reputation is important.
It’s unusual and I’m unsure how likely it is, but it’s possible you might be able to swing some sort of EA mentorship or grant, which might make your second choice more interesting if you can point it toward pandemics.
Overall (maybe because of my bias described below), I generally distrust labs where you are fitted in a slot to solve a problem for a PI. I recommend a lab where you can gain general programming skills or have freedom to network and express your abilities.
More background:
I’m against/biased against a lot of “hard science” grad work, because (maybe outside of a small number of labs/PIs) I think you often just serve as cheap labor that doesn’t actually involve a lot intellectual activity (this is a distinct from the other problem a lot of research activity is winning games in academia).
This might be different and not apply to bioinformatics or something exposed to a lot of programming.
I’d offer a slightly different perspective on this point, as a hard science graduate student doing a lot of cheap labor with most of my intellectual activity done on the side.
When you do cheap “non-intellectual” work in a hard science lab, you learn:
A practical sense of how to design an experiment to answer an important question
An intuition for how to connect data in papers (and content in textbooks) to what the scientists actually did in the lab, and what the limitations, implications, and importance might be in light of that.
The skills to develop and troubleshoot new methodologies
How to collaborate and communicate with your supervisor, colleagues, tech support, other labs, core facilities, and authors of papers you’re building on
A sense of what bottlenecks hold things back in the lab, as fodder for future research directions as well as any possible future management role you may have
I have gotten a ton of value out of both rote lab techniques and the arduous process of troubleshooting methods for a novel experimental design.
Hi Charles, thank you for taking the time to write your reply, you’ve given me some really valuable advice. Those are some valuable general considerations to keep in mind regardless of which direction I go. I know that in the first lab, the third point is especially relevant as I was told that there is a postdoc that will be providing guidance for me throughout, so that’s something for me to be mindful of.
Your fourth point is something I’ll give a bit more thought as well, especially because I know the second lab recently did a project monitoring a population to see which pathogens were present, so there’s definitely scope for me to shape a project in that area.
I appreciate the final point too, that’s something to be careful of and I should try to get a better handle on how much freedom I’ll have before I commit. Hopefully, as you say, that’s not as generally true for bioinformatics-related projects but I can see there still being a risk of that.
Thanks again!