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: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling, representational analysis, or
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scientific goals on our lab website (https://hscrb.harvard.edu/labs/arlotta-lab/). In addition, the Arlotta lab is actively working to foster an equitable and inclusive community. Basic
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-time inference within rendering pipelines Minimum Qualifications: • Ph.D. in Computer Science, Visual Computing, Interactive Media, or related field • Strong C++ and/or C# programming expertise
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 hours ago
, QUDA , Grid) and GPU computing. Experience with statistical analysis of large Monte Carlo datasets, including inverse-problem and Bayesian methods. Experience using DOE computing allocations ( NERSC
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and high-performance or GPU-accelerated computing environments. Applying rigorous methods for external validation, transportability, subgroup performance, fairness, uncertainty quantification, and
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resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Modes of Work Positions that are eligible for hybrid or mobile/remote work mode are at the
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collaborators. We believe that cooperation and collegiality are essential elements for research successes. You can find more information about the team and our work here: https://www.bric.ku.dk/research
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with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflows. Demonstrated technical
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. Develop reconstruction, simulation, and analysis software using modern C++, Python, ROOT, and HPC/Grid computing resources for processing multi-petabyte experimental datasets. Develop and apply state-of
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models, spin-boson models, neural or threshold dynamics, or analogue/digital quantum simulation. 3. Experience with high-performance computing, GPU acceleration, automated benchmarking or large