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within a multi-disciplinary research environment consisting of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing
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, and parallel computing, with a proven ability to work within highly secure and regulated environments. This role involves close collaboration with security teams, scientists, and IT leadership to ensure
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, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in
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frameworks to maintain secure and compliant environments. Document system architectures, processes, and best practices, and contribute to internal knowledge sharing. Participate in on-call rotations and off
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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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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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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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A BS degree in computer science, computer engineering, information technology, information systems, science
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strategic management and strict adherence to security protocols. We are looking for candidates with extensive experience in either classified HPC data center operations, architecture, parallel computing
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration