532 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" uni jobs at University of Sheffield
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Investigating how DNA damage responses combat infections by the typhoid pathogen Salmonella enterica
Srour et al. 2025, EMBO Mol Med (https://doi.org/10.1038/s44321-025-00347-8) ElGhazaly et al., 2023, Cell Reports (https://doi.org/10.1016/j.celrep.2023.113181) Ibler et al. 2023, Nat Commun (https
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, numerate subject (such as maths and physics), including Matlab programming. Full details of how to apply can be found at the following link: https://www.sheffield.ac.uk/acse/research-degrees/applyphd
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. Visit http://www.sheffield.ac.uk/sgs to learn more. Funding Notes First class or upper second 2(i) in a relevant subject. To formally apply for a PhD, you must complete the University's application form
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between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective
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parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
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How does a molecule walk? Computer simulations of molecular machines in action School of Mathematical and Physical Sciences PhD Research Project Directly Funded UK Students Prof Sarah Harris, Dr
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BSM processes. This will involve taking a lead role in developing dedicated software frameworks, including the implementation of machine learning techniques. A long-term attachment (6-12 months) and
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acids institute (https://sheffield.ac.uk/nucleic-acids) and the centre for Single Molecule biology (https://smash.sites.sheffield.ac.uk/), providing additional expertise. This project will contribute
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neurological and cardiovascular disorders. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying References GONZÁLEZ-SANTANA, A., ESTÉVEZ-HERRERA, J., SEWARD
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with the CDT’s aim to achieve a sustainable wind farm lifecycle by developing methods for high-value reuse of composite turbine blades. Machine learning and non-destructive evaluation techniques will be