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for constructing correctness proofs, yet the standard symbolic methods face significant limitations in both expressivity and scalability. This project proposes novel techniques for constructing formal proofs
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impacts of low-carbon transport measures across the Avoid–Reduce–Improve spectrum. Identify barriers and enablers to implementation, embracing both the formal and informal forms of governance that influence
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comprise both the development of bioinformatics pipelines and the application of novel machine learning methods for interpreting microbiome and host ‘omics data from faecal, intestinal biopsy and saliva
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involves formalization in type theory and the mechanization of results using interactive theorem provers. The project welcomes a broad range of perspectives across functional-imperative programming