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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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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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migration and in vivo murine models of inflammation and technical expertise in complex flow cytometry is essential. The previous ability to work with rodents would be advantageous, whilst experience in
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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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. 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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-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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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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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