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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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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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physiologically relevant models will provide crucial platforms to mimic disease pathology, and better understand and treat tendinopathy. The project will generate tendon-chips using in-house commercially available
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defending the cultural value of knowledge for its own sake. You will also possess computational expertise in data mining and / or analysis, ideally including language processing, and be able to work with an
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and statistical modelling, statistical image analysis and computer vision, chemometrics, biophysics, bioengineering. Preference will be given to candidates with a demonstrated experience in applying
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rapid growth of artificial intelligence. Key responsibilities include synthesising 2D materials with precise control over layer thickness, composition, and defect density, followed by comprehensive
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potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence
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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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About the Role The post is based in the Trauma Sciences Research team within the Centre for Neuroscience, Surgery and Trauma. The Trauma Sciences research team (www.c4ts.qmul.ac.uk) provides
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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