41 proof-checking-postdoc-computer-science-logic PhD positions at University of Birmingham; in Uk
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of how to apply to it please visit https://centa.ac.uk/studentship/2026-b20-linking-tree-emissions-to-atmospheric-chemistry-bvoc-reactivity-under-rising-co%e2%82%82/ Further information on how to apply
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. Candidate Requirements A strong academic background in Engineering, Mathematics, Physics, Architecture or Computer Science. An undergraduate degree with at least 2.1 in one of the above subjects is essential
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(e.g., academia, pharmaceuticals/materials industry, data science). Additionally, you will gain research and communication skills, including a strong emphasis on integrating computational and
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of the workflow. While the majority of the project is computer based, there is a small lab-based component in order to generate cell samples to be able to acquire the NMR data. Once proof of concept has been
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Programme (DTP), offering one home and one overseas 3.5-year studentship covering full tuition fees and a standard UKRI stipend. For further details of how to apply, please click on the 'Apply' button above.
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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First-class (or equivalent) degree in Mechanical, Automotive, Powertrain, or Control Engineering, or a closely related discipline. Strong academic performance and research potential are essential
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and programmable biomaterial synthesis. The ability to program the behaviour of biomolecular chemistry is foundational for developing new biotechnology applications. Redox-sensitive molecules are a
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The probabilistic method is a powerful tool which has been especially influential in the fields of combinatorics and computer science. In the context of combinatorics, this method was pioneered by
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quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing