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and has a large group of collaborators. You will be joining a great team of supportive and social PhD students working in a high-quality research environment. Learn More: The Dynamics Research Group
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) Familiarity with machine learning models, for example, large language model (Desirable) *This opportunity will cover UK/Home fees. Overseas students who wish to apply would need to be able to finance
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, manipulate large datasets, visualise data and perform numerical and statistical analysis is a requirement. Experience in handling 'big data', machine learning and working in distributed teams, is useful
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experimentation and validation, and machine learning. References of our current/recent work are here: "Automatic Retrieval-Augmented Generation of 6G Network Specifications for Use Cases," IEEE Communications
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. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
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to test data, which are therefore only useable within a narrow range of scenarios. These limitations result in the requirement of large number of high-cost experiments being conducted to populate the models
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to develop forecasting models. The use of machine learning methods for demand modelling could also be considered. The models that are developed will be implemented in a modelling tool which could be used by
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engineering or a relevant area. An MSc degree and/or experience and good knowledge in gas turbine theory, thermodynamics, Machine Learning, and computer programming will be an advantage. Funding Sponsored by
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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment