103 parallel-computing-numerical-methods-"Prof" Fellowship positions at Harvard University
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The Charles A. King Trust Postdoctoral Fellowship Program supports basic and clinical research on the causes of human disease with the mission of improving its treatment. The program provides
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postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research at the intersection of Geometry
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. Positions are available for recent Chemistry, Physics, Electrical Engineering, Biophysics and Computer Science Ph.D.’s who are interested to work in synthetic artificial biochemistry-free life mimics (life
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and Regenerative Biology has an exciting and broad multiomics program focused on brain aging. Approaches will include experimental and computational efforts across multiple labs at Harvard's Faculty
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for Biomedical Imaging (Harvard/MIT/Mass General). In parallel, there will be opportunities to analyze and publish existing data upon identifying areas of mutual interest. The appointment is for one year with a
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for Biomedical Imaging (Harvard/MIT/Mass General). In parallel, there will be opportunities to analyze and publish existing data upon identifying areas of mutual interest. The appointment is for one year with a
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. accredited colleges, universities, and U.S. Government laboratories across the country. Participants in the IC Postdoc Program have achieved numerous significant accomplishments, including: More than 450
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The Dean's Postdoctoral Fellowship The Dean's Postdoctoral Fellowship Program is administered through Harvard Medical School Office for Diversity Inclusion and Community Partnership whose mission
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especially encourage candidates with proven experience in applying computational and experimental methods to social scientific questions – including aptitude in working with large-scale datasets and text
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric