339 computational-physics-"https:"-"https:"-"https:"-"https:"-"U.S" Fellowship positions in United States
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computational biologists. Publishing research findings in peer-reviewed journals and present at scientific conferences. Mentoring and supervise graduate and undergraduate students in related projects
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Apply Now How to Apply Please send a CV and research summary to tcernak@umich.edu Job Summary We are seeking a highly skilled computational expert to enhance our laboratory's capabilities in
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, computer science, architecture, and engineering to develop scalable, data-informed solutions in sustainable design, construction, and energy management. The Cluster aims to modernize—and ultimately revolutionize
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and Pay . See our Candidate Resources to learn more about our hiring process and what to expect. How to Apply This position will close on the date it is filled Please Note: Depending on your role and
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, Mechanical Engineering, Computer Science, or a related field for biomechanics and assistive robotics is required by the time the position starts. Additional Qualifications Some background in coding, controls
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Fellows: Rubin Lab The Rubin lab in Harvard’s Department of Stem Cell and Regenerative Biology has an exciting and broad multiomics program focused on brain aging. Approaches will include experimental and
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technical/computational backgrounds (ML, NLP, AI safety, working with LLMs) as well as computational social scientists. The ideal candidate bridges these worlds or is eager to learn across them
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Structures: the formal study of mathematical proof itself, as a subject in computational complexity, type theory, metamathematics, logic, and beyond. What is the space of mathematical truth, and what proofs
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Details Title Postdoctoral Fellow in Soft Robotics School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description The Wood Lab is seeking a postdoctoral fellow to explore new methods for embodied intelligence in soft and reconfigurable...
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analysis, PDE-constrained optimization and optimal control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and