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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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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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conditions. Assessing generalization capabilities and robustness against uncertainties. Stability Analysis of Model Predictive Control (MPC) Loop In parallel, we will ensure the stability of the MPC
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workflows, version control, model deployment, monitoring, and documentation. Manage data privacy, security, and compliance considerations (e.g., HIPAA), data usage agreements, and governance for imaging
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and applying optimisation methods, such as model predictive control, multi-objective optimisation and, where appropriate, reinforcement learning Investigating suitable model-reduction and approximation
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, anomaly detection, or predictive maintenance. Experience with reinforcement learning applied to optimization and control. Experience developing, assessing, and validating predictive models. Experience
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control methods such as Model Predictive Control (MPC) are often limited by discrepancies between the nominal model and the actual system behavior. The objective of this thesis is to improve model
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as AWS SageMaker Small or large language model development with retrieval-augmented generation Desirable : Industrial analytics or predictive maintenance Knowledge of construction-related industries
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highly motivated Post-Doctoral candidate to join our research team. Your research focus will be on the experimentation of real-time robust model predictive control schemes for induction welding. As a
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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