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in real materials behave in complex ways, and turning measurements back into quantitative information about a material’s internal state is a notoriously difficult inverse problem. This is where AI can
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organised into bulk structures. Ice, for example, is a regular lattice of water molecules. Self-assembling, hierarchical systems are different. Here molecules combine to form complexes, the complexes form
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Improving the lifecycle of complex domestic waste The increasing use of multilayer materials and mixed-fibre textiles has created significant challenges for recycling, as these materials
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About the project: Machine learning accelerated electronic transport calculations for complex materials Supervisor: Prof. Neophytos Neophytou, University of Warwick Advancements in materials
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administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate 51 Faculty of Physics Startdate: 01.10.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr
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associated with your research including conference attendance, secondments, and other research and training activities. Additional funding is available to support a range of CDT activities, such as secondments
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promotion and athletic performance, essential for maintaining functional capacity and driving physical development. However, prescribing RT is complex due to large inter- and intra-individual variability in
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-throughput computational approaches for practical usage. The project. This project will develop a physics-informed computational workflow for the discovery of novel supramolecular host systems capable
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Additive manufacturing offers significant opportunities to produce complex, lightweight components that would be difficult to manufacture using conventional methods. One important application is the
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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas