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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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Write software to support cutting edge research and facilitate Proof of Concept demonstrations Implement, evaluate, and enhance machine learning pipelines for cybersecurity applications Basic
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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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the following requirements: Contribute to and progressively lead Artificial Intelligence (AI) & Machine Learning (ML) engineering support on an ongoing basis as evidenced by, for example, innovative artifacts
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sciences, national security, and energy technologies, ORNL delivers the scientific discoveries and technical breakthroughs essential to fulfilling the DOE's mission through operational excellence. The ORNL
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, and assisting with conflict resolution efforts under HR leadership guidance. HR Systems & Process Support: Support ongoing enhancements to HR systems, workflows, and internal policies. Utilize Microsoft
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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. Intelligent Sampling for Federated Learning: Investigating frameworks (such as SICKLE) for intelligently sampling cross-facility extreme-scale data to enhance federated learning workflows with platforms like
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, and compliance requirements. Strong aptitude for computer systems, electronic tools, and digital workflows. Ability to learn and adapt to new technologies, including AI-enabled tools used to support
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the final candidate’s qualifications and experience, including skills, knowledge, relevant education, certifications, plus also aligned with the internal peer group. It is not typical for an individual to be