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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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Vitro Models. The project aims to use organ-on-a-chip technology combined with bioengineering approaches to develop, validate and use a suite of vascularised human tendon-chip models. These high quality
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Opportunities to produce high-quality publications Development of multidisciplinary skills in statistical modelling, machine learning, AF imaging and Raman spectroscopy and clinical translation of automated
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. Applicants should have a PhD in Cultural Geography, Environmental Arts or a closely related field; knowledge of current climate- and ocean-related scholarship in the Blue Humanities; a track record of
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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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holder will join a multidisciplinary, diverse, and inclusive team with frequent meetings, interactions, and high-quality mentoring. About You You will have a PhD (or close to completion) in developmental
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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