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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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Qualification Type: PhD Location: Nottingham Funding For: UK Students Funding amount: Full tuition fee waiver pa (Home Students only) and stipend at above UKRI rates pa (currently at £20,780
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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
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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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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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, 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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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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) 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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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