582 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions at University of Sheffield
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for the project prior to submitting your application. [1] https://www.weforum.org/impact/carbon-footprint-manufacturing-industry/ [2] https://www.gov.uk/government/news/government-publishes-uks-third-climate-change
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application form using the following link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying All applicants should ensure that both references are uploaded onto their application as a decision will be
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that the toxin induces DNA damage responses in cultured cells that activates a senescence tumour suppressor mechanism (https://doi.org/10.1038/s41467-019-12064-1). Cells undergoing toxin-induced senescence undergo
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instrumentation for materials formulation, processing, characterisation and performance assessment. More information on these facilities can be found at: https://www.sheffield.ac.uk/royce-institute https
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Matcher (https://www.sheffield.ac.uk/eee/people/academic-staff/stephen-matcher ) and the Human Parturition Research Group of Prof Dilly Anumba (https://www.sheffield.ac.uk/smph/people/clinical-medicine
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Work arrangement Full-time Duration Fixed-term for 3 months, during summer 2026 Line manager Project grant holder Direct reports N/A Our website https://sheffield.ac.uk/cmbe For informal enquiries about
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about this project. Funding Notes We welcome inquiries from: - applicants that have already secured PhD funding - self-funded applicants References https://microbialphysicsgroup.sites.sheffield.ac.uk
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, including computer vision and machine vision. As a project engineer, you will ensure successful project delivery, delivering continuous improvements to IMG processes and build AMRC’s reputation in computer
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(SITraN). Our mission is to uncover the genetic drivers of Amyotrophic Lateral Sclerosis (ALS) by integrating cutting-edge technologies, including single-cell epigenetic profiling and machine learning, with
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of agricultural weeds to herbicdes from an eco-evolutionary perspective. This project will develop models for the evolution of herbicide resistance that combines field data and computer models. The aim is to