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resources and demonstrated ability in applying numerical techniques to water-energy research. Strong candidates will have advanced knowledge and skills relevant to one or more of the following areas: River
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. These include areas such as additive manufacturing, quantum material design, scientific data reconstruction, for material discovery, inverse methods, complex optimization, population and evolutionary dynamics
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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-physics simulations with machine learning algorithms for modeling and optimizing of metallic materials and advanced manufacturing processes. Participate in the design of integrated, scalable numerical
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, or versatility throughout diverse and robust technological environments with a proven ability to work within highly secure and regulated atmospheres. This role involves close collaboration with numerous security
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modeling group at the forefront of deploying novel computational engineering techniques for problems that are critical to U.S. national and energy security. We utilize our expertise in numerical
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modeling group at the forefront of deploying novel computational engineering techniques for problems that are critical to U.S. national and energy security. We utilize our expertise in numerical
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supports the design, performance optimization, and reliability of gas centrifuges used in the processing of uranium and stable isotope compounds. Within this organization, the Distinguished Rotor Dynamics
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; functional, complex, and real analysis; as well as numerical analysis and approximation of partial differential equations. Expertise in optimization; computational fluid dynamics; sparse grid methods
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methods, complex optimization, population and evolutionary dynamics, cyber security and power grid analysis, and uncertainty quantification for engineered systems. Collaboration with experts from various