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- Postdoctoral Fellowship in Reinforcement Learning, Probabilistic Methods, and/or Interpretability
Description
Harvard UniversityPosition Details TitlePostdoctoral Fellowship in Reinforcement Learning, Probabilistic Methods, and/or InterpretabilitySchoolHarvard John A. Paulson School of Engineering and Applied SciencesDepartment/AreaComputer SciencePosition DescriptionAccepting applications for postdoctoral position in Reinforcement Learning, Probabilistic Methods, and/or Interpretability. Information on the lab can be found at finale.seas.harvard.edu and our group's webpage https://dtak.github.io/ We work on probabilistic models, reinforcement learning, and interpretability + human factors.
Basic QualificationsCandidates are required to have a PhD in machine learning, math, stats, physics, or some other technical area by the time the position starts.Additional QualificationsCandidates should have significant experience in some area of statistical inference/optimization, and will have the chance to mentor both undergraduate and graduate students in these areas (as it relates to joint projects).
Special Instructions Required application materials through this site include 2-3 recommendation letters, a statement of research interest, and a current CV.Contact Information
Contact Emailfinale@seas.harvard.eduSalary Range$67,600 $91,826Pay offered to the selected candidate is dependent on factors such as rank, years of experience, training or qualification, field of scholarship, and accomplishments in the field.Minimum Number of References Required3Maximum Number of References Allowed3KeywordsEEO/Non-Discrimination Commitment Statement
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PI281706484