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12 Nov 2025 Job Information Organisation/Company CFM-Centro de Física de Materiales/MPC-Materials Physics Center Research Field Other Researcher Profile Recognised Researcher (R2) Leading Researcher
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control (MPC) under uncertainty for autonomous systems. The research aims to develop state-of-the-art numerical methods for solving challenging belief-space optimal motion planning problems and their
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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developing a Model Predictive Controller (MPC) Help build and test a simulation-in-the-loop setup for the MPC using the calibrated model and real-world data Document and validate models, methods and results
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complicated systems. These include humanoids, quadrupeds, omnidirectional drones and others. These controllers will rely on principles like Reinforcement Learning (RL), Model Predictive Control (MPC), or other
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before Dec 5, 2025 will receive full consideration. About the Max Planck-IAS-NTU Center The Max Planck-IAS-NTU Center (MPC) is new interdisciplinary initiative of the Max Planck Society (MPG) in Germany
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. The work is part of the regional project “Optimizing Renewable Energy Integration: FPGA-Based Model Predictive Control (MPC) for Grid Stability” (Ref. SI4/PJI/2024-00238), funded by Comunidad de Madrid and
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian Multi-Energy System & Grids Team is looking for a Research Fellow in
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of the system to be controlled. This RL approach will be compared and contrasted with optimal control methods such as Model Predictive Control (MPC). The various control strategies developed will first be
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital