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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 6 hours ago
Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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in collaboration with Texas A&M College of Engineering, Texas A&M School of Medicine and the state’s top-ranked Houston Methodist Hospital offer an innovative medical school program called ENMED
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in collaboration with Texas A&M College of Engineering, Texas A&M School of Medicine and the state’s top-ranked Houston Methodist Hospital offer an innovative medical school program called ENMED
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languages; experience with GPU programming (e.g., CUDA) is highly desirable. Background in optimization, image-guided radiotherapy, medical imaging, or computational modeling. Experience with treatment
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Requisition Id 16166 Overview: The Programming Systems Group at ORNL seeks a forward‑leaning Postdoctoral Researcher to advance research at the nexus of Agentic AI, high‑productivity programming
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Qualifications: Ph.D. (within 0–5 years) in computational bioscience, computational biophysics, computer science, or a related field Strong programming skills in C++, Python, or similar scientific computing
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computing software libraries (e.g., Trilinos, MFEM, PETSc, MOOSE). Experience with shared and distributed memory parallel programming models such as OpenMP and MPI. Experience with one more GPU or performance
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University. Dr. Cheng Peng invites applications for a postdoctoral position supported by a Laboratory Directed Research and Development (LDRD) program. This appointment spans a 2-year timeframe. The
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computing environments, and GPU programming. Necessary skills include knowledge of data processing using software (e.g., Matlab, R, IDL) and/or statistical/mathematical programming languages (e.g., R, Matlab
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in top-tier machine learning/AI conferences and/or leading scientific journals. Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch