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contact, as identified by AFRL through recent past efforts. This includes the implementation of relevant algorithms and solvers for distributed GPU computing within the JAX Python library. Qualifications
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modelling, and neuroimaging. The position provides access to high-performance computing resources, including GPUs and supercomputing clusters, for advanced simulations of cerebral blood flow and molecular
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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environment, which brings together more than 400 researchers across disciplines. The collaboration provides access to substantial computational resources (GPU nodes), advanced high-throughput instruments
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, and progression outcomes) and high-end compute (hundreds of NVIDIA H100 GPUs) via Mila and the Digital Research Alliance of Canada, and involves active collaborations with Stanford, Oxford, Google
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RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent written and oral communication skills. Motivated self-starter with
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including code design, documentation and testing. Familiarity with optimization methods including Machine Learning (ML) techniques. Any experience with computations on GPUs. Working knowledge of Linux command
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reasoning or tool-augmented LLMs, RL (RLHF/RLAIF/online RL), or foundation models for science, Software engineering skills (Python) and experience with modern DL stacks (PyTorch) and multi-GPU training
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with GPU-accelerated computation and high-dimensional data analysis. Enthusiasm for applying AI innovations to real biological and medical challenges. Required Application Materials: Cover letter