Postdoctoral Fellowship in Reinforcement Learning, Probabilistic Methods, and/or Interpretability
|Title||Postdoctoral Fellowship in Reinforcement Learning, Probabilistic Methods, and/or Interpretability|
|School||Harvard John A. Paulson School of Engineering and Applied Sciences|
|Position Description|| |
Iím always looking for longer terms postdocs (e.g. 2 years) that will be a good fit for research directions in the lab. You can get a sense of what we do by looking at my webpage finale.seas.harvard.edu and our groupís webpage https://dtak.github.io/ ó we work on probabilistic models, reinforcement learning, and interpretability + human factors. Our websites are also a good place to learn more about us.
Finale Doshi-Velez is a Gordon McKay Professor in Computer Science at the Harvard Paulson School of Engineering and Applied Sciences. She completed her MSc from the University of Cambridge as a Marshall Scholar, her PhD from MIT, and her postdoc at Harvard Medical School. Her interests lie at the intersection of machine learning, healthcare, and interpretability. For more information, please visit: https://finale.seas.harvard.edu
|Basic Qualifications|| |
I expect Postdocs to have completed their PhD in machine learning, math, stats, physics, or some other technical area.
|Additional Qualifications|| |
Postdocs should also already have significant experience in some area of statistical inference/optimization; you will have the chance to mentor both undergraduate and graduate students in these areas (as it relates to joint projects).
A commitment to support diversity, equity, and inclusion/belonging in an academic setting is a must.
|Special Instructions|| |
Please contact me through the joining section on my website to learn about what specific openings I have. The interview process consists of you giving a talk to the lab and then I will follow up regarding additional 1-1 interviews with me, group chats with students in the lab, etc. All qualified applicants will receive consideration.
|Contact Information|| |
|Equal Opportunity Employer|| |
Harvard is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.
|Minimum Number of References Required||3|
|Maximum Number of References Allowed||3|
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