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PhD Studentship: Advancing solid-state battery electrolyte manufacturing with terahertz spectroscopy
electrolytes are key to safer, higher-performance batteries, but understanding and inspecting their microstructural homogeneity and conduction in product-scale electrolytes remains challenging. The project will
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, microstructure, manufacturing processes, and electrochemical performance to develop next-generation iron anodes for rechargeable iron–air batteries. The project combines electrochemistry, materials science, and
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are developing a suite of models to deliver a through-process microstructural and mechanical property prediction framework for steel. By integrating these models into a cohesive digital twin architecture, the work
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manufacturing approaches, investigating melt behaviour, densification, defect formation, and microstructure in materials. A distinctive element of the project is its data-driven approach. Together with colleagues
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of establishing fundamental microstructure-property relationships. Carbon-based and hybrid nanomaterials will also be explored to enhance reinforcement and multifunctionality, with the goal of reducing energy loss
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of investigating the relationships between microstructure, processing routes, and thermomechanical performance. The research will combine physics-based modelling and numerical simulations, including thermomechanical
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): the PhD student will finally work again in Saint-Aulaye on the site of the company CERAQUITAINE. Objectives: understanding how the microstructure of industrial refractory materials controls
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of the solid-state microstructure in semiconductor polymers) Grant Reference: 101086805 (ERC Consolidator Grant PARACRYST) - Martín Pérez, J. Title: Postdoctoral Researcher PERÍODO AYUDA: INICIO: 01/01/2024 FIN
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): the doctoral candidate will work to SAFRAN's site in Colombes (France). Objectives This PhD project aims to investigate the influence of ceramic shell microstructure and cluster architecture on the thermal shock
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at experimentally informed microstructural features. You will combine first-principles modelling and machine-learning approaches to develop predictive simulations of hydrogen behaviour in compositionally complex Fe