34 phd-computer-engineering-"Diamond-Light-Source"-"Diamond-Light-Source" Postdoctoral positions at Pennsylvania State University
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SPECIFICS The School of Engineering at Penn State Behrend is seeking qualified applicants for a one-year postdoctoral scholar position, with the possibility of renewal. Candidates should hold a Ph.D. in
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SPECIFICS The Department of Electrical Engineering is looking to hire a Postdoctoral Scholar for Dr. Qiming Zhang's research group. The minimum qualifications as well as research themes are as follows
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reporting. Candidates must have a PhD in MatSE and experience in mechanics of materials, computational modeling, and experimental characterization of materials. Candidates should submit a CV and cover letter
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Computer Science, Math, Statistics (in hand at time of hire) or a related field are eligible to apply. Life sciences (Biology, Genetics, Biochemistry, etc.) PhDs with computational experience are also
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SPECIFICS The Long-Qing Chen Research Group in the Department of Materials Science and Engineering in the College of Earth and Mineral Sciences is looking for a Postdoctoral Scholar. The postdoc will work
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and Engineering Department at Penn State University. The candidate will be working on a wide range of materials including beam sensitive materials, 2D materials, and oxides and will have the opportunity
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mentoring of graduate students. The required qualifications are: PhD degree in Civil Engineering, Materials Science, Chemical Engineering, or related field Extensive research experience related to concrete
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molecular infectious disease testing. The qualified applicant must have been awarded a PhD within the last 2 years and state their interest in pursuing a career in clinical microbiology to support patient
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research in a highly multidisciplinary team environment. Successful applicants will have experience in surgery biomedical engineering, microfluidics, cell signaling and/or vascular biology. Our regenerative
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the Electrical Engineering department, and Daning Huang in the Aerospace Engineering department in the area of Scientific Machine Learning. The project is to develop computationally efficient reduced-order