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Field
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execution environments, or secure enclaves. Experience with distributed computing, cloud computing, containers, Kubernetes, Docker, Apptainer/Singularity, or high-performance computing environments
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Biophysical and Biomedical Measurement Group - NIST | Gaithersburg, Maryland | United States | 28 days ago
radiological emergency response. This position is within the Biophysical Metrology and Inference program at NIST, which develops measurement approaches, physical models, and data-analysis methods that connect
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scientists to join our interdisciplinary research program investigating the biology of hepatitis B virus (HBV) and the mechanisms that allow the virus to establish and maintain chronic infection. Our long-term
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and funding availability. VISION Position Purdue as a national leader in AI-driven software and systems research spanning edge computing, cloud and distributed systems, high-performance computing (HPC
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Researcher to join our interdisciplinary research program. Our lab investigates how bacteria and human mitochondria maintain homeostasis and respond to stress and environmental challenges. A central focus of
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Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description
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distribution. Both projects will develop the Fontan emulator to be patient-specific, MRI compatible, computer controlled, and quantitatively evaluated using advanced bench-top, CFD/FSI simulation, and MRI-based
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to federal research proposals, sponsored research applications, and collaborative project reports. Familiarity with scalable computing environments, cloud platforms, high-performance computing, distributed AI
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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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, distributed control, formation control, perception, and decision-making in complex environments. Design and implement data-driven approaches, including machine learning, reinforcement learning, surrogate models