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, orchestrating large-scale investments and partnerships, and positioning ORNL as the national leader in geospatial HPC, data infrastructure, and emerging computing paradigms (edge compute, neuromorphic, quantum
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will design and implement differential privacy solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing
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is part of the Geospatial Science and Engineering Division (GSED) at ORNL. The group conducts cutting edge research and publishes from novel machine learning based solutions to large scale geospatial
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analytics, including correlation analysis and machine learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical
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data processing and multigroup cross-section generation tools such as AMPX or NJOY. Experience applying artificial intelligence, machine learning, or surrogate modeling methods to nuclear engineering or
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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret
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this role, you will develop and apply advanced data science, machine learning, and statistical approaches to challenging problems in nuclear nonproliferation and national security. The successful candidate
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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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research environment consisting of computational scientists, computer scientists, experimentalists, and engineers/physicists conducting basic and applied research in support of the Laboratory’s missions
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researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance