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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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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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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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algorithms (convex/nonconvex, stochastic/robust, MPC) for real-time dispatch, frequency regulation, and DER coordination. Integrate data-driven and physics-informed approaches for state estimation, forecasting
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themes include: Safe and efficient decision-making and control for autonomous vehicles. Model Predictive Control (MPC), Reinforcement Learning (RL), and reachability analysis. Uncertainty quantification
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into model-predictive control (MPC) or reinforcement learning (RL) frameworks to compute optimal exoskeleton assistance in real time. Validating the developed methods in human experiments using motion capture
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. These controllers will rely on principles like Reinforcement Learning (RL), Model Predictive Control (MPC), or other Optimized Controllers. Essentially this position is in the realm of Artificial Intelligence (AI
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process industries; advanced process control (APC); model predictive control (MPC); digital twins and real-time process monitoring and control; process analytical technology (PAT); process intensification