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About the role The main purpose of this position is to lead the computational and mathematical aspects of a BBSRC-funded project focused on engineering robust, modular, synthetic microbial
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, medical statistics, or a closely related area such as computer Science, or mathematics. * If PhD pending, it must be conferred ≤3 from date of interview. Significant, relevant research experience Proven
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, process mapping, data analytics, economic modelling, or a useful area of business theory is needed. Previous experience in some of the business and management areas related to the project, which may include
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Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER) You will join the SBIDER group in the School of Life Sciences and the Mathematics Institute at Warwick University. We are a
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Contract Type: Fixed Term contract up to November 2026 Background The Sustainable Manufacturing Group at the University of Birmingham leads research that advances cleaner, smarter, and more efficient manufacturing. Our work focuses on reducing energy use, decarbonising industrial processes, and...
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from candidates with a strong interest in climate science and ice dynamics. You should have: A background in physics, applied mathematics, Earth science, or a related discipline Skills in numerical
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks, diffusion models) Proved experience in working independently and as
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component of the project is to build transdisciplinary theory and practices for coastal community resilience across diverse stakeholders in the UK and within the project team. The successful candidate will
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human behaviours AI governance modelling AI race dynamics Interpretable and beneficial AI Gaming and Game Theory Intelligent, secure and reliable systems Contact point for inquiries: Professor The Anh Han
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skills and experience of collecting and analysing qualitative and survey data, ideally using Qualitative Comparative Analysis, in the context of theory-based, mixed methods impact evaluation of complex