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Field
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transformer architectures (e.g., ViT/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g
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-on experience developing generative models Is highly proficient in PyTorch and/or JAX Has experience training large-scale neural networks on HPC or GPU clusters Has experience with representation learning and
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chemistry and experience with quantum chemistry packages (e.g., Molpro, NWChem) Strong skills in developing and implementing computational and numerical methods; familiarity with parallel computing on CPU/GPU
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experimental data and workflows. The team drives innovation in algorithm design, GPU-accelerated computing, and quantum-ready methodologies applicable to complex scientific problems across the experimental
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(static and dynamic photochemistry, heterogeneous catalysis, modeling of interfaces and ionic liquids). It benefits from access to the CBPSMN mesocenter, with a large amount CPUs and GPUs facilities. In
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required to maintain a new GPU cluster at KMI, spending no more than 20% of the FTE. The anticipated starting date is between April 2026 to October 2026. The appointment will be initially 2 years and may be
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research practices Experience training and deploying machine-learning models on GPU-based systems; familiarity with HPC environments is an advantage Interest in interdisciplinary research at the interface
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their publications Experience programming GPUs with CUDA, SYCL, HIP or OpenMP Experience using and developing code with AMReX Experience in performance engineering to improve code scalability and reduce time-to
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and writing scientific code - Knowledge of at least one of the parallel programming paradigms (MPI, OpenMP, GPU) - Proficiency in both spoken and written English is essential (work will be carried out
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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming