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) Applications are invited for a three-year PhD studentship. The studentship will start on1st Jan, 2026. Project Description Glioblastoma (GBM) is the most aggressive and treatment-resistant form of brain cancer
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In the “Research Proposal Section” of the online application simply state that you are applying to the open position on “Machine Learning for Probabilistic Modelling” with Dr Edward Gillman and
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models. PIML can learn from small amounts of data and are more immune to hallucinations than conventional AI, making them exceptionally suited for biomedical applications. Research Environment You will
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the decision analytic modelling field research field and contribute to high quality reports for funding bodies and peer-reviewed outputs. You will hold a DPhil/PhD in health economics or a related quantitative
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the decision analytic modelling field research field and contribute to high quality reports for funding bodies and peer-reviewed outputs. You will hold a DPhil/PhD in health economics or a related quantitative
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& robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees
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region and identify mutations. Develop and optimise bioinformatics tools to detect mutations using positive controls. Apply polygenic risk scores (PRS) to genome-wide SNP data to identify individuals
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. The successful candidate for this PhD position will be involved in the experimental and theoretical development of a new technology called Surface Nanoscale Axial Photonics (SNAP), which will enable
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will then embark on coming up with a suitable design for a generic economic model and construct the model using their software of choice. This new generic model can then be demonstrated by using data
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Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon