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the context of algorithmic problems related to constraint satisfaction and graph homomorphism and isomorphism problems. It brings to bear significant new mathematical (algebraic and topological) methods
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animals, while Prof Durbin's works on computational genomics and large scale genome science, including the development of new algorithms and statistical methods to study genome evolution. Moving forward
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modern Bayesian modelling frameworks such as Stan, Turing.jl, and PyMC, including automatic differentiation frameworks, MCMC sampling algorithms, and iterative Bayesian modelling. Special attention will be
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our software development team, developing novel scientific algorithms and applications in the areas of spectroscopic analysis and mining of the science data catalogues extracted from the pipelines
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organisational goals. Stay at the forefront of AI advancements, translating breakthroughs into actionable solutions. Develop robust algorithms and tools to analyse structured and unstructured data and improve