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Field
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Identifying and validating models for complex structures featuring nonlinearity remains a cutting-edge challenge in structural dynamics, with applications spanning civil structures, microelectronics
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of the studentship is to measure and model the optical properties of facial skin at different wavelengths and angles and to relate the results to other work by the research team on the clinical measurement and
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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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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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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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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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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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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
; University of Sussex | The City of Brighton and Hove, England | United Kingdom | about 1 month agoprotein, including the interplay between its domains and its diverse, non-canonical functions, remains elusive. This project will build directly upon Professor Giamas's findings to dissect the molecular
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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