49 machine-learning "https:" "https:" "https:" "https:" uni jobs at University of Sheffield
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digital-first principles. By fusing materials science, machine learning, and advanced simulation, the project offers an exciting opportunity to redefine how we engineer and deploy functional surfaces
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of dark matter particles in the decay of the gluinos. The search will be performed using the full ATLAS Run-3 13.6 TeV proton-proton collision data. The student will gain expertise in machine learning and
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the third Higgs boson decays to two tau leptons, or another highly sensitive combination. The student will gain expertise in machine learning techniques for signal-background discrimination and will
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survival is shaped by resource availability, competition, and cultural knowledge of food resources. You will work with an established bioacoustic dataset, a validated call machine-learning classifier, and a
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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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; Communicating Data; Data Translation; Data Modelling; Databases and Beyond; Practical Programming for Data Science; and User Centred Design and Human Computer Interaction You will also carry out supervision of UG
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with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers. https://www.yorkshirebiosciencedtp.ac.uk Project Description: The highly
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, advanced statistical methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and
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, brain-inspired computation. Supervisor Bio Professor Eleni Vasilaki is a computational neuroscientist who has spent years building bridges between biological learning theory and machine learning. Her most
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“An improved machining temperature prediction model for aerospace alloys: Effect of cutting edge radius and tool wear”, Journal of Manufacturing Processes 133 (2025) 1100–1110. https://doi.org/10.1016/j.jmapro