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Distributed, robust and adaptive model predictive control (MPC) School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr P Trodden Application Deadline: Applications
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predictive control, adaptive modelling, and advanced dynamical systems and contribute to the rapidly changing landscape of adaptive control. At the same time, you will also help develop advanced control
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. This limits our ability to predictably control and exploit the drop for useful tasks. Aims: 1. Develop computational models to quantitatively predict the response of chemically active drops to the
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to develop learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor
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predictions and control-oriented execution (<1 ms execution time). Advanced Control Synthesis: Synthesize and computationally test model-based (Model Predictive Control - MPC), data-driven
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, sustainability, and advanced process control, aiming to develop AI-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and
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, reliability, and autonomy Design and implement advanced control strategies (e.g., model predictive control, adaptive control, feedback/feedforward control, ML-based controls) for thermal system
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technologies, predictive modelling and digital control systems create new opportunities to address this problem. By combining real-time measurements of fruit and its environment with models capable of
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health data science. Primary responsibilities Design, implement, and validate predictive models that use imaging data (e.g., CT, MRI, PET) to forecast response to adjuvant treatments. Build end-to
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data-driven modeling. • Experience in model predictive control or reinforcement learning is preferred. • Good publication record in energy-efficient cooling or AI-assisted energy systems is desirable. Req ID: 33224 Apply now