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development, testing and modelling of biochars at our laboratories in Edinburgh. This is strategic work that we seek to expand, in enabling responsible scaling of biochar solutions at global level. This post is
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). The post is funded by NIHR and is fixed-term for 24 months, with a possible extension. This project is about creating novel AI models to predict patient outcomes following acceptance or refusal of an offer
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of original machine-learning based algorithms and models for multi-modal ultrasound guidance that are intuitive for a non-specialist to use while scanning and trustworthy. You will work with clinical domain
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these pathways modulate T-cell signalling, activation, and effector functions in preclinical models of autoimmunity. This research is part of a broader effort to define how inhibitory receptors tune T-cell
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, activation, and effector functions in preclinical models of autoimmunity. This research is part of a broader effort to define how inhibitory receptors tune T-cell responses in health and disease, ultimately
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record in studying humans and machine learning models, in the context of human social behaviour, learning, decision-making, or a related area. A proven track record of publishing work as lead author in
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, or a related discipline, and must have a strong research track record, experience with cell culture model systems, proficiency in flow cytometry, confocal microscopy or other advanced imaging techniques
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Duration: 8 months or until 31 May 2026, whichever is sooner About the Role This is a research position for an EPSRC funded project entitled “Distributed Acoustic Sensor System for Modelling Active
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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 previously unthinkable
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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