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Field
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position as POSTDOC (F/M/X) in the Optimization and Optimal Control Group . (Full-time employee) for an initial period of one year, with starting date to be arranged. Your Tasks The full-time position is
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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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learning, to accelerate CFD optimization and enable adaptive control strategies for complex urban wind conditions. From an industrial standpoint, the objective is to deliver a cost-effective and efficient
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modeling, control, and optimization of power and energy systems with applications to maritime and coastal infrastructures (e.g., shipboard microgrids, port facilities, islanded communities, desalination
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conditions. To achieve this, the project explores advanced machine learning approaches, including surrogate modeling and reinforcement learning, to accelerate CFD optimization and enable adaptive control
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and/or interest in one or more of the following: optimization and optimal control (esp. optimal control of partial differential equations (PDE) or PDE-constrained optimization), scientific machine
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for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in
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optimizing high-performance C++/C# software modules for real-time control, sensor fusion, and data analysis; developing unity‐based visualization and user-interaction interfaces, including 3D modelling
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., model-based, optimal, learning-augmented control), perception, and planning modules; run structured experiments and benchmarking. Architect high-quality research codebases in C++/Python/C# (e.g., ROS/ROS
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primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or