221 high-performance-computing-postdoc Fellowship research jobs at Harvard University
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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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surveys; · Programming/scripting knowledge suitable for processing raw data for analysis (e.g., text manipulation); · One or more computational environments for statistical analysis (e.g., MATLAB, R
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at internal meetings, and contribute to recommending next steps for experiments. Take an active role in strategy discussions related to projects. Maintain a high level of expertise through continuous engagement
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computational (bioinformatics) tools on human and mouse tissues and using in vitro methods on human cells, to explore the consequences of genetics variants on human biology. This is a multi-year position
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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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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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to develop an innovative research program in sequential decision making. Our lab is involved in digital health studies in dental health, cardiac health, physical activity, mental illness and substance abuse
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and Streptococcus pneumoniae. We welcome applications from recent PhD graduates who are interested in contributing to our highly collaborative and exciting research program. The successful candidate
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. Responsibilities: 1. Conduct research in neuroscience using in vivo and in vitro models. 2. Publish and present research findings in high-quality scientific journals and conferences. 3. Collaborate with faculty
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of transmembrane signal transduction. Collaboration with team members specializing in molecular biology, computational modeling, and cell biology is encouraged, as is the presentation of research findings in