"On being asked to write about post-doctoral plans by my grad school".

Postdoctoral Proposal Requirement

In the fourth year of study, students prepare a description (up to one page in length) of their plans after graduation and how the doctoral education the student has received will be utilized in the future.  You should include some articulation of the "skill set" you have acquired during your graduate training and comment on how these skills could be used in the next phase of your career. 

This is what I wrote grudgingly........

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Graduate school has been exciting so far. The greatest lesson has been a first-hand experience of how the initial fluttering of an idea, after being churned over and over again, slowly begins to take shape and eventually transforms itself into something more tangible. It has been equally fascinating to observe people around me going through the same process and doing it differently. I have learnt from seeing others as much as I have learnt by doing it myself.

It is rather hard to be explicit about what skills I have acquired along the way. I certainly don’t feel like I have acquired much. Some confidence in my ability to think, a bit of time- management, a lot of people-management, a little more reverence for deadlines and an ability to handle acute sleeplessness. Yes, I have also acquired some experimental skills like building microscopes, putting wires in the brain, etc but that is beside the point!

I am confident that I will be able to think my way through a problem and wish to pursue academic research as a post-doctoral fellow in neuroscience. I don’t really know what I would like to study. I am fascinated (mostly since yesterday since I started thinking about the future) about how a population of neurons in a brain region goes about doing its job, especially in the context of decision-making. The ability to monitor large ensembles of neurons with 2-photon imaging in a carefully designed behavioral task, could give us some insight into the workings of the decision making process. Bayesian approaches have been quite successful as a broad description of how the brain works. It is necessary to bridge the gap between such Bayesian models and the mechanics of how it is realized in the brain. It would be highly interesting to find such algorithms being implemented by a population of neurons. These ideas are extremely premature and moreover I really don’t understand the Bayesian approaches that well yet. I hope to have a better understanding of what I really want to do when I am ready to embark on that journey.