404 computer-programmer-"https:"-"https:"-"https:"-"I.E" positions at Harvard University
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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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Details Title Postdoctoral Research Fellowships in the Science and Theory of Generative Modeling School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer
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, machine learning and AI, statistical computing, big data and AI applications and prediction in biology, medicine and infectious diseases. Potential research projects include (but are not limited
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have strong computer skills. Must have a demonstrated ability to work collaboratively in a team environment with multiple staff members. Must be able to handle complex and confidential materials and
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of developmental and/or regenerative neurobiology, neuronal cell biology, and/or nervous system disease involving the cerebral cortex to join an ongoing research program focusing on the developmental, degenerative
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. Processes, organizes and summarizes data, reporting results using a variety of scientific, word processing, spreadsheet or statistical software applications or program platforms including R, SAS, Python, and
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and at national/international conferences. Collaborate with an interdisciplinary team of biostatisticians, computer scientists, and climate scientists. Contribute to open-source code, reproducible
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and
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responsible experience in program leadership, student engagement, public service, or related fields. Additional Qualifications and Skills: Experience working in or closely with city government strongly
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assessment, and ambulatory behavioral assessments to precisely track brain and cognitive change over short intervals. The program of research seeks to understand individual differences in aging trajectories