11 phd-in-simulation-engineer Postdoctoral positions at Pennsylvania State University
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computers or atomic-physics platforms, and quantum algorithms for quantum many-body physics. A PhD in Physics is required. The ideal candidate will have numerical simulation skills with exact diagonalization
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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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calculations Materials modeling/electronic structure calculations Machine Learning/Deep Learning techniques. Education and Experience: A PhD in physics, astronomy, or a closely related field must be completed
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), and in vivo fiber photometry (TDT). We are particularly looking for a PhD-level systems neuroscientist with expertise in animal behavior tracking using deep learning algorithms and their causal link
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%): Generating academic outputs, such as presentations, grants, and manuscripts. 4) Related tasks (10%): Other related tasks assigned by the principal investigator. Eligibility: Candidates holding a PhD degree in
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creative, career-driven candidates to join our group. Candidates must hold a PhD with at least 5-year experience as a postdoc in the field of bone biology or RNA biology. A Strong publication record and
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the fields of RNA biology, bone biology, and osteoimmunology. Candidates must have a PhD. A Strong publication record and significant experimental training in molecular biology and biochemistry are required
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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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mechanisms of hepatitis B virus replication and persistence in the laboratory of Jianming Hu, MD, PhD, Professor, Department of Cell and Biological Systems, H-107, The Pennsylvania State University College
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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