42 computer-programmer-"Prof"-"University-of-Bradford"-"Prof" Postdoctoral positions at Duke University
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experience in optics, computation and electronics. Responsibilities will include designing and implementing optical systems based on quantitative phase imaging with digital holography and electronic systems
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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
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research or scholarship. The appointment is generally preparatory for a full time academic or research career. The appointment is not part of a clinical training program, unless research training under
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may include teaching responsibilities. The appointment is generally preparatory for a full-time academic or research career. The appointment is not part of a clinical training program, unless research
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is generally preparatory for a full-time academic or research career. The appointment is not part of a clinical training program, unless research training under the supervision of a senior mentor is
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specification that accompanied a major life history transition. We are looking for someone with experience in evolutionary developmental biology and strong computational skills, ideally with experience in
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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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. The postdoctoral associate must hold a PhD in mathematics, mathematical/computational biology, or a closely related discipline awarded before August 1st, 2026. Applicants with experience in mathematical modeling
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, United States of America [map ] Subject Areas: Chemistry / Bioinformatics , Chemical biology , Computational Appl Deadline: 2025/10/29 11:59PM ** Position Description: Apply Today is the last day you can apply for
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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