377 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at Harvard University
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PLEASE NOTE- Applications MUST be submitted to the Harvard Academic Positions website in order to be considered. https://academicpositions.harvard.edu/postings/15491 The Gravity, Spacetime, and Particle
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Details Title Fellow in Human-Machine Interface Clinical Research – Bionics Lab School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Bioengineering Position
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determined by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . Create a Job Match for Similar Jobs About Harvard University Harvard University is devoted
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of and experience with quantitative methods (simulation modeling, optimization, and machine learning) preferred This is an annual term position reviewed each academic year on or before June 30th, with
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PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes of collective
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ssantagata@bwh.harvard.edu Salary Range Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, can be found at https://postdoc.hms.harvard.edu/guidelines Minimum
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) Description: Apply Description Join us as a postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health
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) is a community of Information Technology professionals committed to understanding our users and devoted to making it easier for faculty, students, and staff to teach, research, learn, and work through
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on performance. Please take a look at the type of work Prof. Stantcheva does on her website: https://www.stefanie-stantcheva.com/ as well as on her Social Economics Lab website http://socialeconomicslab.org
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supervision and mentorship of Dr. Zitnik, the Associate will: 1) Explore and learn about state-of-the-art techniques for constructing, maintaining, and contextualizing biomedical datasets by reviewing recent