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of the correctness of software and hardware systems using machine learning. Recent advances in neural certificates—such as neural model checking and neural termination analysis—have shown promising results in
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”, through analysis of MR Diffusion Weighted Imaging and MR Spectroscopy and leading other members of the wider team Specific aims of the project To prospectively evaluate non-invasive pre-surgical radiomic
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September 2025 Background To support the advancement of NIHR funded research project through data collection and analysis, contributing to knowledge generation, and to provide research assistance to projects
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to completion) in Computational Materials Chemistry or a related discipline Experience in Defect Chemistry Analysis Experience in Python Programming Expertise in Electronic structure analysis of solids Expertise
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supervision Conduct qualitative data collection, including semi-structured interviews and/or focus groups Analyse and interpret qualitative data using appropriate methods (e.g. thematic analysis) Use
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in metal additive manufacturing using laser powder bed fusion and/or electron beam melting and/or powder processes, such as hot isostatic pressing, cold compaction. CAD experience (preferably
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responsible for the entire project from polymer synthesis, crystallisation-driven self-assembly, characterising data collection interpretation. Training will be provided in all of the component techniques but
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to data analysis and biological interpretation. Role Summary Develop, implement, and apply advanced reproducible computational tools and workflows to process, analyse, and interpret large-scale LC–MS-based
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electrochemistry and flow in a range of systems, including multivalent metal ion batteries, redox flow batteries, electrodialysis and capacitive deionisation. This project is highly interdisciplinary and
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areas or equivalent qualifications Proven experience in structural biology, preferably by Nuclear Magnetic Resonance Spectroscopy, both in data acquisition and analysis Track record of working