21 data-"https:"-"https:"-"https:"-"https:"-"Inserm"-"Gustave-Roussy-" 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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overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
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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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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
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FFA on different type of metallic surfaces as a function of temperature and concentration. The modelling data and principal component analysis will be used to build property-structural relationships
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will