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), where heat is stored/released through reversible chemical reactions. This project focuses on NaOH water TCES systems, which use cheap, abundant materials [1]. We will develop modelling tools that combine
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions
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modelling (e.g. PHREEQC or COMSOL) and/or with scientific programming (e.g. R, Python, MATLAB or similar) is an advantage. • Experience in working with cementitious materials is an advantage. • Competence in
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evolution at high temperature. The transition towards smarter, lower-carbon manufacturing demands new ways to understand and monitor how materials evolve during processing. This PhD project addresses a
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models, host–pathogen interactions, and molecular and cellular immunology. Experience with immunological techniques such as flow cytometry, cytokine analysis, multiplex immunoassays, ELISA
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, engineered powders, including Cermetal and WC-Co-based materials, will be investigated as energy-absorbing media within the damping system. The development combines computational modeling, machine learning
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semiconductor research and innovation facility (the £55M Centre for Integrative Semiconductor Materials); In collaboration with and sponsored by the National Physical Laboratory (NPL), with a co-created research
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challenges of our time. Big Questions, Real Impact – From climate modelling and sustainable energy to advanced materials and biomedical systems, HetSys projects apply cutting‑edge computational and
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models, host–pathogen interactions, and molecular and cellular immunology. Experience with immunological techniques such as flow cytometry, cytokine analysis, multiplex immunoassays, ELISA
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characterisation, to generate data-rich descriptions of evolving materials and processing pathways. A central aim is to couple these experiments with machine learning, mechanistic modelling, and automated data