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viruses. In parallel, the candidate will also develop and evaluate vaccines against these pathogens in animal models. The candidate will be responsible for writing proposals and publications, and training
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours ago
approaches include MHD, Vlasov, and hybrid codes run on computers ranging from PCs to massively parallel computers, while analytical work involves linear and nonlinear instability theories. Location: Goddard
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develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running
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system. We have a variety of ongoing projects that leverage high throughput reporter assays (e.g., Massively Parallel Reporter Assays) to understand gene regulation in the living brain. We apply
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ongoing projects that leverage high throughput reporter assays (e.g., Massively Parallel Reporter Assays) to understand gene regulation in the living brain. We apply these approaches to understand
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. Previous experience in computational modeling of atmospheric aerosols and parallel computing/software development is strongly desired. The term of appointment is based on rank. Positions at the postdoctoral
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redox balance. Our studies explore how p53 integrates metabolic cues by acting as both a sensor and regulator of cellular metabolism. In parallel, we are identifying metabolic changes that promote tumor
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to analyzing data Knowledge of high-performance computing, such as parallelization, the use of C++, or interfacing with specialized linear algebra packages Other Information: Work arrangement: On-site Candidates
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scales through component-based parallelism. The focus of the post-doc position is to design and implement this pipeline -- developing native reasoning for rich combinatorial constraints, creating tools
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning