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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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that make these problems tractable, as well as computational tools and laws governing biological system behavior. We welcome applications from recent PhD graduates who are interested in these or related
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and cell differentiation. We develop technologies to generate statistical data sets that make these problems tractable, as well as computational tools and laws governing biological system behavior. We
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place across the departments of Physics, Chemistry and Chemical Biology, Mathematics, and the School of Engineering and Applied Sciences. Active research areas include quantum information and computer
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one year with a possibility of renewal based on performance. Basic Qualifications Candidates must have a PhD in physics, biology, or a related field by the time of appointment. The ideal candidate will
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; Geology; Health and Medicine; Mathematics/Statistics; Materials Science and Physics; Psychology and Psychiatry; Technology, Data, and Computer Science; and Zoology.
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have a PhD in physics, biology, or a related field by the time of appointment. The ideal candidate will also have demonstrated experience in machine learning and biological data analysis and a strong
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Program and will work closely with the staff of the Image Collection and Fieldwork Archives (ICFA) to process the Justin Kerr Collection, which consists of photographs of approximately 6,000 unique
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disruptions on mental and physical health outcomes across three longitudinal cohorts (HPFS/NHS/NHS2). Dr. Denckla uses her training in clinical psychology and epidemiology to investigate: 1) biopsychosocial
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articles, (e.g. for JSTOR Daily where we publish a Plant of the Month feature) and peer-reviewed articles. The fellow will also manage the editorial process for external submissions. Support and Development