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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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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
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with transistor-level circuit design and simulation What We Offer We provide GPU computing for machine learning on Aalto's Triton high-performance computing cluster, alongside industry-standard
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largement utilisés dans l'industrie ou au sein d'équipements médicaux. You may be interested in these vacancies Stage - Bac+5 - Implémentation d’un solveur de diffusion dans un code GPU non structuré
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d'intégrité de structures industrielles sans les dégrader. You may be interested in these vacancies Stage - Bac+5 - Evaluation d'algorithmes en précision mixte sur GPU pour un solveur dense - H/F Ref