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Demonstrated research experience with HPC, AI/ML and/or distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and
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experiments to neutron reflectometry studies. The main objectives are: (1) To implement fluorescence microscopy techniques and Flicker spectroscopy to studies of DIBs under electrical stimulation; (2) Use other
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Science or related discipline. Demonstrated experience in scientific data visualization, AI/ML, or a related field. Proficiency in the Python and C++ programming languages. Preferred Qualifications: Strong publication
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: Strong publication record with publications in journals/conferences related to scientific visualization, scientific computing, and/or uncertainty quantification. Strong experience in using techniques
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: Experience in one more of the following areas: Mathematical tools for data analysis Numerical methods for differential and integral equations Modern machine learning software tools and frameworks
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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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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: PhD in
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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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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