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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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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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) 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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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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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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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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Application deadline: 15/08/2025 Research theme: Computer Science No. of positions: 1 Eligible for: UK This 4-year PhD project will be funded by DLA studentship and is open to UK students
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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