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Application deadline: All year round Research theme: Computational Chemistry, Material Science No. of positions: 1 Eligibility: UK students This 3.5-year project is fully funded by industry and home
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) methods to tackle challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division, our group advances generative models, molecular simulations
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highly motivated candidate to develop models integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing
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. The development of a physically-based model of energetic material will overcome the current limitations and provide predictive capabilities that are crucial for the understanding of the behaviour of novel
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integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing, computational model development, data processing, and
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materials. The computer modelling of LSP remains challenging due to its multi-physics and multi-scale nature. The dependency of the process on the shape of the laser pulse, its energy, ablation layers etc. is
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Vacancies PhD position: Green Steel; predictive micro-mechanical models to for steels including increased levels of scrap Key takeaways Societal demands on reducing the environmental impact of
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obtaining) a PhD in Engineering or other relevant area Knowledge and experience in: Material flow analysis, environmental assessment, emissions accounting or other topics and tools of Industrial Ecology
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materials for synthesizing different types of hydrogen storage molecules. Using advanced quantum mechanical calculations, you will develop multi-scale models to study reaction kinetics and improve catalyst
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to the full development pipeline: from algorithm design and implementation to clinical integration and evaluation. You will also work on improving prognostic models using (neuro-symbolic) AI and develop