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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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data and finite element modelling is a plus. You will have experience in writing publications, and be happy working in a collaborative environment. About the School The role will be based at the School
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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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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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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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with large-scale longitudinal datasets to explore gene–environment interplay and developmental risk pathways. The successful candidate will join a vibrant research team based at Royal Holloway
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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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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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About the Role The ATLAS L1Calo/Global upgrade project is primarily a firmware development based on a common multipurpose hardware module. It requires a lot of software development in a wide range