comment by rodeo_flagellum ·
2021-07-23T21:41:44.538Z · LW(p) · GW(p)
I have being reading content from LW sporadically for the last several years; only recently, though, did I find myself visiting here several times per day, and have made an account given my heightened presence.
From what I can tell, I am in a fairly similar position to Jozdien, and am also looking for some advice.
I am graduating with a B.A. in Neuroscience and Mathematics this January. My current desire is to find remote work (this is important to me) that involves one or more of: [machine learning, mathematics, statistics, global priorities research].
In spirit of the post The topic is not the content [LW · GW], I would like to spend my time (the order is arbitrary) doing at least some of the following: discussing research with highly motivated individuals, conducting research on machine learning theory, specifically relating to NN efficiency and learnability, writing literature reviews on cause areas, developing computational models and creating web-scraped datasets to measure the extent of a problem or the efficacy of potential solution, and recommending courses-of-action (based on my assessments generated from the previous listed entity).
Generally, my skill set and current desires lead me to believe that I will find advancing the capabilities of machine learning systems, quantifying and defining problem afflicting humans, and synthesizing research literature to inform action, all fulfilling, and that I will be effective in working on these things done as well. My first question: How should I proceed with satisfying my desires, i.e. what steps should I take to determine whether I enjoy machine learning research more than global priorities research, or vice versa?
It is my plan to attend graduate school for one of [machine learning, optimization, computer science] at some point in life (my estimate is around the age of 27-30), but I would first like to experiment with working at an EA affiliated organization (global priorities research) or in industry doing machine learning research. I am aware that it is difficult to get a decent research position without a Master's or PhD, but I believe it is still worth trying for. I have worked on research projects in computational neuroscience/chemistry for one company and three different professors at my school, but none of these projects turned into publications. This summer, I am at a research internship and am about to submit my research on ensemble learning for splice site prediction for review in the journal Bioinformatics - I am 70% confident that this work will get published, with me as the first author. Additionally, my advisor said he'd be willing to work with me to publish a dataset of 5000 fossils image I've taken of various fossils from my collection. While this work is not in machine learning theory, it increases my capacity for being hired and is helping me refine my competence as a researcher / scientist.
Several weeks ago, I applied to Open Philanthropy's Research Fellow position, which is a line of work I would love doing and would likely be effective at. They will contact me with updates on or before August 4th, and I anticipate that I will not be given the several follow-up test assignments OpenPhil uses to evaluate its candidates, provided that their current Research Fellows have more advanced degrees and more experience with the social sciences than I do. I have not yet applied to any organizations whose focus is machine learning, but will likely begin doing so during this coming November. This brings me to my final questions: What can I do to increase my capacity for being hired by an organization whose focus is global priorities research? Also, which organizations or institutions might be a good fit for both my skills in computational modeling and machine learning and my desire to conduct global priorities research?
Any other advice is welcome, especially advice of the form "You can better prioritize / evaluate your desires by doing [x]", "You seem to have [x] problem in your style of thought / reasoning, which may be assuaged by reading [y] and then thinking about [z]", or "You should look into work on [x], you might like it given your desire to optimize/measure/model things". Thank you, live well.