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frameworks scientific machine learning, inverse problems, differentiable simulation, or physics-based modelling computer vision, biological image analysis, microscopy data, or other noisy high-dimensional
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desirable): Autonomous laboratories for chemistry, materials, biology, etc. AI/ML for predictive modeling and inverse design Generative models, reinforcement learning, and agent-based approaches to
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; agent learning and adaptation; reinforcement learning and inverse reinforcement learning. o Machine Learning & Intelligence, including machine learning and adaptation; deep learning; computer
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; agent learning and adaptation; reinforcement learning and inverse reinforcement learning. o Machine Learning & Intelligence, including machine learning and adaptation; deep learning; computer
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support; agent learning and adaptation; reinforcement learning and inverse reinforcement learning. o Machine Learning & Intelligence, including machine learning and adaptation; deep learning; computer
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Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from Mathematics (Inverse Problems), Computer Science
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
, QUDA , Grid) and GPU computing. Experience with statistical analysis of large Monte Carlo datasets, including inverse-problem and Bayesian methods. Experience using DOE computing allocations ( NERSC
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, inverse problems, linear and multilinear algebra, mathematical analysis, meshing, modeling & simulation, multilevel/multiphysics/multiscale methods, numerical analysis, operations research
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Hyperbolic PDEs;Inverse Problems in Chemical Engineering; Computational Geometry of Surfaces. Process Dynamics and Control (CHEE672): Having taken CHEE672 or equivalent graduate core course in Process Dynamics
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-assisted theorem proving, formal verification, proof assistants, automated proof search, and AI-guided mathematical discovery; neural operators, physics-informed learning, inverse problems, uncertainty