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consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, protected veteran status, or any other
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candidates with a background in political science, economics, modern history, sociology, anthropology, law, business, and other disciplines bearing on the study of globalization to apply. The postdoctoral
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. Training also includes an introduction to various advanced neuroimaging methodologies. Essential qualifications for these positions include: a Ph.D. in Neuroscience, Computer Science, Bioengineering
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design, earthquake engineering and material science are a plus; - A very good publication record and strong project management skills. The term of appointment is based on rank. Positions
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materials:1) a cover letter of application2) a curriculum vitae3) a sample of writing in the candidate's field of specialization4) contact information for three or more references Applications received by
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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the development and testing of new materials. The work will involve reactor design and setup with gas flow capability and process optimization. Qualified candidates should have a Ph.D. in chemistry, physics
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other subfields in political science, related disciplines, or in interdisciplinary areas. While most political theorists are trained in departments of politics and political science, we welcome
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, normative analysis, and intellectual projects involving work jointly with other subfields in political science, related disciplines, or in interdisciplinary areas. While most political theorists are trained
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials