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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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Research Assistant or Postdoctoral Research Associate About the Role This is a research position for an EPSRC funded project entitled “Distributed Acoustic Sensor System for Modelling Active Travel
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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
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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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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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sensing, signalling and memory, critically influences the disease onset and progression1. The Iskratsch Group , at the School of Engineering and Materials Science, Queen Mary University of London is
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sociomaterial practices that shape decision-making around medicine use in pregnancy. The research will be informed by theoretical perspectives such as Science and Technology Studies (STS), including concepts
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close to completion) in Materials Science, Physics, Chemistry, Nanotechnology, Electrical Engineering, or a closely related field, with a strong background in the synthesis and characterisation of 2D