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outcomes Synthetic data generation (virtual patients) Statistical model checking to ensure statistical correctness of the results Machine learning–based classification and regression methods The candidate
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to machine learning. This PhD provides a unique opportunity to shape emerging concepts in Artificial Intelligence Informed Mechanics (AIIM), combining fundamental research with methodological innovation. You
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-state model will be approximated using machine-learning surrogates and will be used for a real-time optimization, such that the plant operates optimally despite disturbances. The candidate will be part of
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modelling, and high-performance computing. The position offers close supervision, access to modern computational infrastructure, and collaboration opportunities across disciplines — from mechanics to machine
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25th February 2026 Languages English English English The Department of Materials Science and Engineering has a vacancy for a PhD Candidate in machine learning and large language models (LLMs
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(viability, proliferation, outgrowth, and invasion assays) is desirable. Experience with, or interest in, machine learning for the analysis of microscopy data and a strong ability to collaborate with
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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, especially large language models (LLMs), enable the development of interactive AI agents that can support human learning in complex, safety-critical environments. Human-centered AI in this project means
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emphasizes principled modeling, reproducible experimentation with open datasets and simulations, and publication-ready contributions targeting leading venues in machine learning and wireless communications
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to research, development and demonstration of a methodology for building and integrating machine learning solutions for past technical artefacts. Contributing to the development of holistic view of product