20 data-"https:"-"https:"-"https:"-"https:"-"Université-Paris-Saclay-GS-Physique" PhD positions at The University of Manchester
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may be removed before the deadline. Modern low-carbon energy systems such as photovoltaic (PV) arrays and battery energy storage systems (BESS) generate extensive measurement data (electrical, thermal
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on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI
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platforms, e.g. aerial drones, climbing robots, and remotely operated underwater vehicles, for capturing degradation data across turbine blades, towers, foundations, and subsea cables; (2) develop a machine
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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platform and/or its manipulator(s) will be used to trace the emission source, using a combination of sensor data, gas behaviour models, and robotic navigation techniques. The project can be tailored
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information about the CDT is also available. Informal enquiries can be made by emailing rainz@manchester.ac.uk . Deadline: Friday 15 May 2026 Start Date: Monday 21 September 2026
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in porous geological formations. The successful candidate will develop and implement computational models, validate them against experimental or field data where available, and contribute to the design
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control performance and efficiency. This PhD project focuses on data-driven analysis of confined liquids structure, informed by total neutron scattering. The emphasis is on developing new analysis
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University Belfast, University of Manchester, University of Edinburgh and University of Bristol. BioAID will train the next generation of scientists in Artificial Intelligence and data-driven approaches
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation