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Field
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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models, shadows and twins, that reduce carbon emissions in transport. Working with a wide range of experts from universities and industries across the UK, you will help create a “digital twinning” system
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AI models to automate QA/QC processes, enabling real-time verification, anomaly detection and consistency checks across records. Develop a cloud-based verification platform for digital data collection
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vivo approaches include patient derived xenograft mouse models, xenograft models, humanized mouse models and subcutaneous mouse models. Perform experimental design, quantitative analysis, literature
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(e.g., SEM path analysis models, growth modeling, latent class analysis, multilevel models), experience with multiple methods (e.g., observational; qualitative; intervention research), and strong writing
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on Digital Twinning Technologies for Transport Decarbonisation. About the Role We are looking for a Research Fellow to help develop new digital tools, including digital models, shadows and twins, that reduce
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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faculty Position Location: Taipei, T'ai-pei, Taiwan [map ] Subject Areas: Atmospheric Science Modeling & Simulation Climate Change Climate Science Appl Deadline: 2026/04/01 04:59 AM UnitedKingdomTime
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, and advanced sequencing technologies to generate large-scale datasets. In parallel, you will develop and apply computational pipelines that integrate sequence analysis, structural modeling, and
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply