20 coding-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dr" "Dr" "Dr" "Dr" Postdoctoral positions at Oak Ridge National Laboratory
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to commit to ORNL’s Research Code of Conduct. Our full code of conduct and a statement by the Lab Director’s office can be found here: https://www.ornl.gov/content/research-integrity Basic Qualifications
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(https://www.olcf.ornl.gov/frontier ) and plant phenotyping (https://www.ornl.gov/appl ). GPTgp is a pilot project initiated in September 2025 with funding from the US Department of Energy and will
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, presentations, reports, and code documentation. Align behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a
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of advanced materials. Research efforts will include the application of density functional theory packages and in-house codes, and the development of supplemental numerical tools, to describe
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Computational/theoretical chemistry and/or physics, chemical engineering, materials or a closely related field completed within the last 5 years. Preferred Qualifications: Experience with coding, electronic
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standard (version control, unit testing, continuous integration, etc.). Experience in the development of large-scale physics simulation codes, including coupling of multiple codes, and an understanding
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scientific outputs that may include peer-reviewed publications in top-tier water journals, professional scientific code/software contributions, and high-quality datasets. Candidates must also be willing
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coding (Python) for building energy modeling and controls Preferred Qualifications: Expertise in modern optimal control techniques (e.g., AI based controls) High level of competence in coding and scripting
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those skills to a variety of problems, and the ability to determine and understand the broader context of his or her research. Preferred Qualifications: Proficiency in multiple modern coding languages is
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., code interpreters, simulation frameworks, databases, lab instruments) and evaluation for long-horizon tasks. Experience with RL and post-training (reward modeling, preference learning, offline/online RL