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Postdoc Research Associate - Machine Learning/NLP for PEM Fuel Cell & Electrolyzer Materials Develop
discoveries and their translation into energy, environment, and security solutions for the nation. Major Duties/Responsibilities: Develop and apply machine learning models to optimize the synthesis
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training to staff and radiological control technicians to support the Lab’s science mission. Major Duties/Responsibilities: Create, schedule, and close out classes in the learning management system Develop
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, stairs, fixed ladders, floor load checks, crane and hoist support systems, etc. in both nuclear and high-risk facilities Prepare engineering reports, studies, calculations, review, and approve internal
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Requisition Id 13188 Overview: We are seeking a Postdoctoral Research Associate who will develop and apply computational methods based on electronic structure theory approaches with emphasis
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interns; Network and develop collaborative R&D with other groups and divisions internally and with DOE, industry, and utilities; Prepare and present research results to sponsors, peer reviewers, and others
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independently in conducting research under general guidance and are expected to prepare manuscripts for scientific publication and present the work to sponsors and at conferences. The position will allow
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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
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Laboratory (ORNL) and UT created to prepare interdisciplinary leaders in energy, science, and technology and develop a world-class workforce for industry, government, and academia that will drive innovation
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designed or prepared for the nuclear fuel cycle.Develop associated control, destruction, or demilitarization plans. Develop subject matter expertise in the major areas of concentration involving export
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) is seeking a scientist to develop and apply Computational and Artificial Intelligence (AI)/Machine Learning (ML) methods to advance hydrologic modeling and Earth system predictability. The successful