515 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" positions at University of Sheffield
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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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and Interview Ability to prioritise work while maintaining standards Desirable Application and Interview Basic computer skills, use of emails and ability to use online training modules. Desirable
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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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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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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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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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Overview We are looking to appoint a Postdoctoral Research Associate who applies strong immune-biology skills to advance a drug development programme for auto-immune and immune-ageing diseases
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characteristics, including personality and motivation, are likely to influence both learning processes and long-term performance. This project addresses these questions using a dynamical systems approach to