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or model parallel training. Experience with multi-physics simulations on HPC and with ML models. Experience working in a multi-disciplinary research environment. Demonstrated written and oral
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for Science @ Scale: Pretraining, instruction tuning, continued pretraining, Mixture-of-Experts; distributed training/inference (FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation
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research teams to install, port, optimize, benchmark, and tune scientific applications and toolsets. Support software environments for a wide range of computational workflows, including parallel and
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, patents, journal papers, and conference publications, participate in proposals, and estimate costs. Basic Qualifications: A PhD degree in physics, optical or electrical engineering, nuclear engineering, or
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architectural design decisions for massively parallel heterogeneous systems, including pathways that integrate quantum or analog co-processors. Performance Engineering at Scale: Profile, model, and optimize
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, including parallel computing frameworks, C/C++, Julia and/or python Experience with training AI surrogate and/or inverse models Experience working in underground laboratories and/or clean rooms Excellent
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, enhanced sampling, QM/MM) Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation
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-performance computing environment, including multi-tenant compute, high-speed interconnects, and parallel filesystems Special Requirements: This position requires the ability to obtain and maintain a clearance
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. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: PhD in nuclear engineering or closely related field and
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, and measure success. Basic Qualifications: A PhD in Nuclear Engineering or a closely related field. Knowledge of nuclear reactor analysis R&D is required as demonstrated through research and associated