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, generative modeling, graph neural networks, transformers, or foundation model pretraining and fine-tuning is a plus · Experience with multi- GPU or distributed training is a plus · Ability to
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will leverage GPU computing on the LUMI supercomputer, one of Europe’s fastest, to perform state-of-the-art molecular simulations. The position includes secondments to industry partners for
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; while they are trained using GPU-based optimization methods, the actual information processing takes place without a GPU, making them energy-efficient and fast. Diffractive networks are to be designed
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Vision-Language-Action models and their fine-tuning, evaluation, or extension. Multimodal sensor integration (force, tactile, vision) into policy learning pipelines. GPU-parallel simulation, large
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realizations of the proposed latent reasoning algorithms. Quantitative comparisons with different hardware backends. Design conventional (GPU-based) deep neural networks for comparison. Publish research
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) or HPC programming (MPI, OpenMP, CUDA, GPU computing) is considered an advantage but is not required Motivation to conduct excellent scientific research and publish in leading international journals
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Preferred skills/knowledge includes: Active TS/SCI Clearance Master’s degree Training and optimizing ML algorithms on GPU hardware architectures, specifically NVIDIA based Working with geo-spatial
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: Active TS/SCI Clearance Master’s degree Training and optimizing ML algorithms on GPU hardware architectures, specifically NVIDIA based Working with geo-spatial data Statistics, multivariable
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simulation - H/F Ref. : 2026-41885-S2269 Internship Lot (46) GRAMAT Stage - Bac+5 - Implémentation d’un solveur de diffusion dans un code GPU non structuré - H/F Ref. : 2026-41610-S2214 Internship CDI
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and graduate research assistants, and presenting at design reviews and project milestones. Conducts benchmarking and performance evaluation of LLM serving and agentic workflows on high-performance GPU