32 mathematics-statistics-postdoc Postdoctoral positions at Oak Ridge National Laboratory
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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relevance to clean energy, climate resilience, and infrastructure planning. Postdocs benefit from access to world-leading high-performance computing facilities and a deeply interdisciplinary research
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workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in evolutionary biology, plant biology, genomics, bioinformatics, mathematics, statistics, computer
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radiochemical processing, isotope separations, or nuclear systems. Experience with statistical design of experiments and integrated experimental/computational research approaches. Ability to work effectively
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problems. Major Duties/Responsibilities: Perform processing and analysis on multimodal data streams, including RF, optical, and spectral signals Develop statistically defensible algorithms to identify and
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residency requirement, you will be required to obtain a PIV credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and
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, applied mathematics, computer science, or a closely related field, completed within the last five years. Demonstrated expertise in computational methods such as finite elements or finite volume techniques
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, radiological health, medical physics, nuclear engineering, applied mathematics or a closely related discipline) Sound foundation in radiation transport, behavior of radionuclides in biological systems, and/or
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field completed within the last five years. Good track record in scattering theory, quantum many-body theory, thermodynamics, statistical mechanics, or non-equilibrium physics. Experience in
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mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design