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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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platforms at our prestigious Centre for in vitro Predictive Models (https://www.cpm.qmul.ac.uk/ ), and work with project partners based at the Cross Institute Advanced Tissue Engineering (CREATE) lab
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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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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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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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CV (see this guide from the Royal Society ) and shortlisted candidates will receive the interview questions in advance. For an informal discussion about the post, please contact Claire Kennan
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narrative CV (see this guide from the Royal Society ) and shortlisted candidates will receive the interview questions in advance. For an informal discussion about the post, please contact Claire Kennan
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Grant “Advancing Gravitational-Wave Astronomy Using Artificial Intelligence” led by Dr Greg Ashton. The successful candidate will join Royal Holloway’s Astronomy Group and conduct research activities
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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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in the laboratory, in patients and in populations. BCI is internationally renowned in many areas of cancer research and it combines ground-breaking basic research with the expertise of clinicians and