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and delay-differential equations. Scientific computing and numerical analysis Large-scale dynamical systems High-performance computing Parallel numerical algorithms GPU computing Strong programming
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massively parallel computing systems, including GPU-accelerated platforms. Develop and maintain scientific software, primarily in Python , using modern practices for software development, testing
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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Max Planck Institute for Physics, Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
. Our long-standing analysis program focuses on exploring the properties of the Higgs boson and searching for physics phenomena beyond the Standard Model. In parallel, the group is engaged in hardware
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candidate will have extensive experience with mathematical optimization formulations and algorithms and their application to physical systems. Additionally, the candidate will be familiar with parallel
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NVIDIA Nsight, rocProfiler, or similar. Strong understanding of parallel programming with MPI, OpenMP, and GPU-based programming models. Experience working on national or international supercomputing
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candidate will conduct model verification, sensitivity analysis, calibration, validation, performance evaluation, and troubleshooting. The position requires strong scientific programming and software
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boundary layer methods (Kármán momentum integral method) • Numerical methods for partial differential equations • Iterative solvers for linear systems • Fluent programming in Python and/or C
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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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Albert Einstein College of Medicine (Bronx, NY) | New York City, New York | United States | about 19 hours ago
stem cells. 2) Functional genomics of noncoding regulatory variation Projects will use massively parallel reporter assays, CRISPR-based screens, and genome editing to test noncoding variants, map