907 computational-physics "https:" "https:" "https:" Postdoctoral positions in United States
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space-based systems, including large satellite constellations. A recent PhD in physics, engineering, computer science, or other relevant fields and strong interest in technical and policy research
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chemistry, condensed-matter physics, many-body physics, material science, quantum information science, quantum and precision measurement, and quantum optics. We are seeking a postdoc to carry out experimental
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Utilized Physical Demands and Work Environment Overview Statement Posting Details Special Instructions to Applicants Quick Link to Posting https://jobs.rutgers.edu/postings/254101 Campus Rutgers University
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Type of Position Temporary Position Time Status Full-Time Required Education Graduate of an ACPE accredited Doctor of Pharmacy Program Required Related Experience Licensed or eligible for licensure in
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: Ithaca, New York 14850, United States of America [map ] Subject Areas: Physics / Elementary Particle Physics , Experimental High Energy Physics , Experimental Particle Physics Appl Deadline: 2026/02/15 11
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for three references is required. To learn more about CITP's fellows' program please visit our website (https://citp.princeton.edu/programs/fellows/). CITP is committed to building a culturally diverse and
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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research with young children Experience with computational methods (e.g., Bayesian modeling, drift diffusion modeling, etc.) Equipment Utilized Physical Demands and Work Environment Overview Statement
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U.S. News & World Report, and our primary care program was ranked 17. The university’s physical therapy, pharmacy and public health programs also are ranked among the top in the country. Read more
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physical environments. This position focuses on research at the intersection of computer graphics, generative AI, and robotics, encompassing topics such as generative modeling, reinforcement learning, multi