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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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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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extracellular histone, a damage-associated molecular pattern that is implicated in adverse outcomes after injury. This project will include using a range of clinically relevant models as well as in vitro
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disease progression. About You Applicants should hold a PhD degree or equivalent in biological or related science and have a strong background in immune cell biology and animal models of inflammatory and/or
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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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-edge machine learning techniques will be used, including Large Language Models (LLMs). About Queen Mary At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the
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the use of mathematical modelling of infectious diseases. The post-holder will work closely with partners within the Global Polio Eradication Initiative to ensure that research is focused towards supporting
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for an enthusiastic and highly motivated Research Fellow to join the world-leading tuberculosis (TB) Modelling group at LSHTM. The successful candidate will be supervised by Dr Rebecca Clark and Prof Richard White and
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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