72 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Duke University
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments
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, United States of America [map ] Subject Areas: Machine Learning Computer Science Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Appl Deadline: none (posted 2025/08
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Postdoctoral Researcher to work on projects related to digital holography and optical coherence tomography. Candidates should have a PhD in electrical or biomedical engineering or related disciplines with
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healthcare. Qualifications Required: PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field. Strong background in machine learning
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in
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. Candidates with non-US degrees may be required to provide proof of degree equivalency. Preferred Qualifications: A PhD or MD/PhD (or equivalent) in biological sciences (cell & developmental biology or a
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cancer and aging settings with CODEX multiplexed imaging. Required Qualifications at this Level Education/Training PhD Experience See job description for requirements. Skills See job description for
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% to supervising and assisting PhD students. Qualifications • Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control