127 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" Fellowship positions at Harvard University
Sort by
Refine Your Search
-
research assistants (RAs); Machine learning skills; Writing papers for management and economics journals; Interest in reskilling initiatives; Working with partner organizations or companies. Basic
-
network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering Candidates should have demonstrated publication records in cardiac and vascular engineering or
-
native peoples, or peoples of African, Asian, or Hispanic descent. The fellowship includes the requirement to teach one course per year (ideally in the fall term), to participate in a fellowship program
-
Details Title Postdoctoral Fellowship Position in Visual Computing at Harvard University School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Sciences
-
design (using computer-aided software). Special Instructions A cover letter and current CV are required as part of the application. SEAS is dedicated to building a diverse and welcoming community, and we
-
to prioritize work and coordinate research protocols with lab members Excellent attention to detail and organization skills Interest in learning and strengthening existing skillsets Special Instructions We highly
-
the auto market, especially electric vehicles. The position will be under the supervision of Professor James Stock and Dr. Elaine Buckberg, and it will be housed in the multidisciplinary Salata Institute
-
, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status. Special
-
, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status. Additional Qualifications Special Instructions Application
-
postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health, including experimental design and reinforcement