552 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions at University of Sheffield
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Effective and Efficient Visual Presentation of Machine Learning Outputs Derived from High-Dimensional Data to Clinicians (S3.5-SMP-Alix) School of Medicine and Population Health PhD Research Project
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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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the Light Microscopy Facility (https://www.sheffield.ac.uk/lmf). Wild-type and mutant lines will be crossed to transgenic lines of interest for live fluorescence imaging. There will also be the opportunity
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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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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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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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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