149 parallel-computing-numerical-methods-"Prof" Fellowship positions at Harvard University
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University seeks outstanding postdoctoral fellow applicants to join a collaborative research team focused on developing crisscross DNA micron-scale structures for applications in optical computing. In
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applications for a Postdoctoral Fellow position working with Dr. Yi-Qiao Song in Prof. David Weitz’s lab on nuclear magnetic resonance (NMR) technology development and applications in subsurface exploration and
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southern Africa. Familiarity with methods for collection and analysis of common disease survey and surveillance data, especially HIV. Experience using modern Bayesian computing and probabilistic modelling
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internal and external meetings Contribute to recommendations on next steps for experiments while also taking an active role overall in program strategy and alignment with the sponsor’s contractual
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imaging and microscopy methods, and perform computational data analysis. Previous experience in translational cancer biology or immunology, spatial biology, and microscopy are preferred but not required
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postdoctoral fellow to explore new methods for embodied intelligence in soft and reconfigurable robots. Basic Qualifications Doctoral Degree in Electrical Engineering, Mechanical Engineering, Bioengineering
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Director of LISH (Dr. Ramona Pop). The position involves conducting rigorous empirical research using field experiments, large-scale data analysis, and computational methods to advance our understanding
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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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computer science, statistics, operations research, or related computational fields. As part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel
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collaborations among operations researchers, statisticians, and computer scientists to overcome the methodological challenges posed by the misalignment between historical methods underpinning modern data science