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) Familiarity with mathematical programming solvers (e.g., Gurobi, CPLEX) or probabilistic/Bayesian computing frameworks (e.g., Stan, PyMC) Ability to formulate high-impact, novel and well-defined research
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to approximate expensive forward and adjoint simulations while preserving underlying physics. Uncertainty-aware inference: combining physics-informed learning for regularization with probabilistic generative
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