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Field
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programming skills in Python, MATLAB, C++, or similar scientific computing environments, including high-performance and GPU computing. A strong scientific track record, evidenced by high-quality publications
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 1 month ago
and development opportunities, and wellness programs, UNCW is committed to helping you thrive both at work and in your personal life. Hiring Range $91,500 - $105,766 Working Title Research Computing and
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of Texas (a defined benefit retirement plan) Additional Voluntary Retirement Programs: Tax Sheltered Annuity 403(b) and a Deferred Compensation program 457(b) Flexible spending account options for medical
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support the operation and development of the Institute’s Linux-based compute infrastructure, including CPU and GPU clusters, storage systems, data centre hardware, networking interfaces, software
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Max-Planck-Institut für Struktur und Dynamik der Materie | Hamburg, Hamburg | Germany | 2 months ago
computing technologies and accelerator (GPU) computing. Familiarity with scientific software toolchains, including compilers, numerical libraries, parallel programming frameworks, and scientific software
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-performance computing, GPU acceleration, automated benchmarking or large-scale numerical workflows. 4. Evidence of self-motivated contributing to collaborative research outputs, open-source software, preprints
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of quantum materials under far-from-equilibrium conditions, and many body quantum information science that have prospects for advantage over classical computation. Techniques employed span over non-equilibrium
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, optimization, or high-performance computing is highly desirable Experience with quantum software frameworks (e.g., Qiskit, PennyLane, Cirq) or HPC programming (MPI, OpenMP, CUDA, GPU computing) is considered
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-throughput processing. Enhance the computational efficiency of compute environments by optimizing resource allocation (including CPU/GPU utilization), parallelizing data pipelines, and resolving processing