-
. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
-
credible claims of quantum advantage. Develop and apply physics-informed AI/ML and digital-twin capabilities to improve modeling, parameter inference, uncertainty assessment, and adaptive feedback between
-
-generation, data-driven manufacturing systems that integrate artificial intelligence, real-time sensing, and digital twins to transform how critical components are designed, produced, and qualified
-
) methods for modeling and optimization of metallic materials and advanced manufacturing processes. Participate in the design of integrated, scalable numerical methods and uncertainty quantification. Follow
-
committed team of scientists to develop composite pipe technologies for geothermal energy or similar harsh environment applications. Design, develop, fabricate, and test composite material in lab scale and
-
candidate will support research and development projects that advance the state of the art in machining science, machine tool design and characterization, manufacturing process optimization, and digital
-
. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
-
relationships via digital manufacturing practices Basic experience with AI/ML techniques Publish research in peer reviewed journals and conferences Support R&D staff members on their projects General support of