748 computer-programmer-"https:"-"Inserm"-"FEMTO-ST" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Harvard University in United States
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travel to New York to support Shop Builds and/or other show-related preparations for up to 2 weeks at a time. Physical Requirements: Ability to sit, type, and work at a computer for extended periods
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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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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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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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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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assigned. Job-Specific Responsibilities: Create and maintain FileMaker Pro (FMP) database of event invitees, the main event tracking tool. Significant amount of computer work, downloading invitation pools
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with students, faculty, and administrators in delivering academic support programs. Excellent organizational skills and ability to plan and prioritize tasks and projects. Flexibility, openness
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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
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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