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-1000 experiment. Develop new low-background materials, scintillators, and production techniques, including custom synthesis of PEN and clean additive manufacturing processes Integrate AI/ML
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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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, 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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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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of simulation software to model complex system flows in processes relevant to the nuclear nonproliferation mission space. You will be expected to be a heavy end user of the tools generated, participate in model
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scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
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orchestration (e.g., Airflow, Prefect, Dagster, Make/Snakemake) and working in Linux/HPC environments. Experience with large, multi-resolution geospatial datasets and performance-oriented processing (tiling
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management of High-Performance Computing (HPC) systems within a classified environment. We are looking for candidates with experience in HPC architecture, cluster management, and parallel computing, with a
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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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-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