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Fully-funded PhD Studentship: Adaptive Mesh Refinement for More Efficient Predictions of Wall Boiling Bubble Dynamics This exciting opportunity is based within the Fluids and Thermal Engineering
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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UKHSA surveillance datasets to see if OTC sales can be used to monitor GI infection activity and better predict outbreaks. The PhD offers the unique opportunity to develop skills in analytical
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. The subsequent data will then be used to populate machine learning models to predict which molecules to synthesise next, to maximise the binding affinity of the molecules to a target protein. This research aims
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building typologies. This research aims to transform Pulse testing through AI integration—specifically leveraging descriptive, predictive, and generative modelling techniques—to enhance test accuracy
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that predict Litz wire behaviour across electrical, thermal, and mechanical domains. Supported by the MTC’s advanced wire braiding platform, the PhD work will pave the way for next-generation ultra-high speed
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future propulsion systems. There is also opportunity for successful candidate to collaborate with experimental teams for materials synthesis, characterisation and validation of computational predictions