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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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accepted all year round Details Dynamic optimization is integral to many aspects of science and engineering, commonly found in trajectory optimization, optimal control (e.g. model predictive control, MPC
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reconstruction using Fourier domain optical normalization." Light-Science and Applications 5: el 60389, 2016. http://dx.doi.org/10.1038/Isa.2016.38 Henn MA, et al: "Optimizing the nanoscale quantitative optical
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, or a closely related field, with a strong record of research productivity. The ideal candidate will have expertise in reinforcement learning, optimal control, vision-language models, or Bayesian
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) Machine Learning and Combinatorial Optimization (subject to personal qualification employees are remunerated according to salary group E 13 TV-L) starting at the earliest possible date. The positions are
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systems, power converters, and propulsion drives. Knowledge of control systems, optimization techniques, and stability analysis is highly preferred. Familiarity with marine electrical systems or isolated
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working at University of Houston More Jobs from This Employer https://main.hercjobs.org/jobs/21995925/post-doc-fellow-power-system-optimization Return to Search Results
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closed-loop optimization. Benchmark AI-assisted methods against classical control approaches and assess scalability and robustness under realistic noise conditions. 2. Neural Ansätze for Quantum
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control theory Knowledge of machine learning or optimization methods is a plus Proficiency in programming (Python, Matlab, C++, or similar) Strong interest in cutting-edge research on 6G and autonomous
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and implement robotic control systems Optimization of formulation for UV 3D printing / direct ink writing 3D printing Contribute to ongoing soft robotics research Design and prototype using CAD software