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Field
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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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of computer science fundamentals including algorithms, data structures, and object-oriented programming. Proficiency in C/C++ or similar language Working with large codebases Containerization (Docker) and building
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applications. You’ll help design, train, and evaluate AI systems that plan, reason, and take actions to accelerate scientific discovery across domains (materials, chemistry, climate, fusion, biology, and more
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers