27 machine-learning "The University of Edinburgh" research jobs at Harvard University
Sort by
Refine Your Search
-
Details Title Postdoctoral Fellow in Biology of Learning and Memory School Faculty of Arts and Sciences Department/Area Psychology Position Description The Gershman lab at Harvard University is
-
-wide in-person events, typically in New York. Basic Qualifications Researchers holding a PhD related to programming languages, artificial intelligence, and machine learning. Researchers with experience
-
to work in a diverse laboratory environment Demonstrated experience engaging with others effectively in learning and applying appropriate practices Excellent organizational, communication, computer and
-
at present is to understand the circadian clock in terms of the integrated functions of its handful of multi-protein machines, recently characterized in our laboratory. This effort is principally based
-
, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status. Create a
-
and/or aging. Must be proficient with standard computer software such as Word, Excel, and PowerPoint. Proficiency in LaTeX encouraged. Must be willing to learn other software as needed (e.g., SAS, R
-
Degree in Biological or Animal Science is preferred. Familiarity with basic computer applications and programs (e.g. Word, Excel) required. Excellent interpersonal and both written and oral
-
timely manner. Demonstrated computer skills using Microsoft Office suite and the internet and willingness/ability to learn and adapt to new software and technologies. Ability to work independently in a
-
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
-
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