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, qualification, and deployment of AI agents and models, Computational Fluid Dynamics (CFD) simulation codes, and Finite Element Method (FEM) based tools for nuclear energy (fission and fusion) applications
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. 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
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through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to advance
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component (ELM) and its peatland-specific configuration (ELM-Peatlands), with a focus on improving the representation of nutrient cycling dynamics and their coupling to hydrological and ecosystem processes
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component (ELM) and its peatland-specific configuration (ELM-Peatlands), with a focus on improving the representation of nutrient cycling dynamics and their coupling to hydrological and ecosystem processes
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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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, etc.) composites. Hands-on experience with lab scale polymer synthesis and analysis. Preferred Qualifications: Experience with computer modelling systems such as finite element analysis (FEA
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. 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
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LiDAR, IMU, camera, and wheel-odometry data in GPS-denied, low-light environments. Implement LiDAR-based or LiDAR-inertial SLAM, factor-graph or pose-graph optimization, loop-closure validation, drift