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project is to develop a series of surrogate models focusing notably on Physics-Informed Neural Networks to emulate the process of sediment deposition, diagenesis, and potentially fracturing, working closely
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In Vitro Predictive Models to Explore Tendinopathy”. The project is funded by the Medical Research Council (MRC) and part of the organ-chip research work underway within the Centre for Predictive in
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, epidemiology, or statistics. The post-holder should have experience analysing data in large developmental cohort studies, experience with using structural equation modelling and network modelling applied
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2025. We seek to recruit a Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based
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project investigating mechanosensing in Diptera. This post will focus on using detailed wing geometry models and kinematic measurements in computational fluid and structural dynamics simulations to recover
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responsibilities will include: Pre-registering data analysis plans; Leading and conducting advanced statistical analyses (e.g., twin/family designs, genomic and epidemiological methods, longitudinal modelling
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experimental approaches to develop and validate novel in vitro and ex vivo approaches that model arterial medial calcification without using any animal products. This work will represent an exciting step forward
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infrastructure enables recruitment of 200-300 severely injured patients annually as part of the ACIT study. We also have a well-established experimental modelling group with full ethical approvals in place for all
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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complex study. Applicants must have a PhD in a relevant subject. The study requires substantial skills in cell and molecular biology and will require vivo testing of the newly created cell-models. Omics