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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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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
. The researcher(s) will be provided access to state-of-the-art supercomputing facilities with advanced GPU and data storage capabilities. Additionally, opportunities will be available for collaborations. Duties
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tasks across distributed infrastructures. A key aspect of the position involves integrating and exposing hardware accelerators, such as GPUs and FPGAs, in a seamless and portable way. This includes
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-dependent approaches. -Proficiency in high-performance computing (MPI/OpenMP/GPU) and scientific code development is a plus. -Interest in attoscience and/or matter–antimatter physics is an asset. -Ability
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://hpcdocs.hpc.arizona.edu/) resources including access to CPU and GPU hardware. Additional access to HPC resources at leadership compute facilities will be readily available to the successful candidate as part of external
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, superconductivity, cryogenics, or microwave electronics. Additional experience beyond the PhD is not required. US citizenship is not required. What we offer State of the art on-site high performance/GPU compute
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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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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing (HPC
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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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communication with a record of leading and reporting results. Desired Qualifications: Knowledge of quantum computing algorithms. Familiarity with tensor network methods. Experience programming GPUs. Experience