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Field
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programming proficiency in Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA
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independently, and solve complex problems, Desirable qualifications: Experience with Vision-Language(-Action) models or physics-informed ML. Experience with GPU programming, NVIDIA Omniverse, Isaac Sim, or other
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/GPUs. These devices provide massive spatial parallelism and are well-suited for dataflow programming paradigms. However, optimizing and porting code efficiently to these architectures remains a key
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. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The EPAD project – Electron-Positron Attosecond Dynamics in Exotic Systems
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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing (HPC
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Arab Emirates Application Deadline 8 Nov 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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Application Deadline 16 Oct 2025 - 00:00 (UTC) Type of Contract Other Job 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
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 3 hours ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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computational fluid dynamics (DNS, LES, or RANS) and/or high-performance computing (MPI, GPU, or parallel solvers), as demonstrated by application materials. Evidence of peer-reviewed publications in fluid