51 machine-learning "https:" "https:" "https:" "https:" "https:" positions at University of Sheffield
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work with the UK semiconductor industry. The studentship represent a unique opportunity to be trained in the epitaxy process and to work in an emerging and exciting area of combining AI/machine learning
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develop our composite machining team. In this pivotal role, you will lead a diverse group of staff, fostering their skills in CNC programming and operation to efficiently undertake research relating
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for MND which could be translated into the clinic. The idea is to use cutting edge machine learning to create clinically actionable predictions such as the time from diagnosis to requirement for a
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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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. The overarching aim of the project is to use machine learning methods to understand why many people who are referred for treatment will drop out prematurely. To do this, two studies are planned. One will use 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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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