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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
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, structural equation modelling, visualisation, preferably in R Competences in quantitative research methods – ideally knowledge of several of the following aspects of quantitative data analysis: experimental
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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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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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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) 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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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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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