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state-of-the-art high heat flux testing, simulating the extreme environments of fusion reactors. Harness advanced computational tools to model complex particle-material interactions and predict material
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cultures—a powerful 3D ex vivo model—this project will dissect the mechanistic links between mTOR signalling, reactive glial phenotypes, and complement activation. The project will also incorporate human
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spaces and habits for them. This is a highly interdisciplinary project that combines computational modelling and behavioural science. The first part will be based on the use of state-of-the-art
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failure before components are built? We invite applications for a fully funded PhD project to develop microstructure-aware simulation models for fatigue and damage prediction in turbine wheels. Working in
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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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industry, a Regulatory Authority or relevant doctoral/post-doctoral research. We are seeking candidates with experience of: Communicating complex scientific issues, in writing and face-to-face, with clarity
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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Numerical simulations of Lattice QCD DoS Dr. Craig McNeile (craig.mcneile@plymouth.ac.uk , tel.: +441752586332) 2nd Supervisor Dr. Vincent Drach ( vincent.drach@plymouth.ac.uk , tel: +441752586335