201 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions in United Kingdom
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the biomechanics of current treatments for craniosynostosis. The role will use advanced approaches including computer simulation, manufacturing, imaging, sensing, and in vivo experimentation. Applicants should hold
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. Coordinate modelling activities across multiple projects and deliver high-quality outputs on time. Integrate new methodologies, including AI and machine-learning approaches, into simulation design. Conduct
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/September 2026 start, 1 post for February 2027 start)¿ will primarily undertake small group teaching of student doctors in years 1 and 2 of the MBChB curriculum, including case-based learning and teaching of
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implementing the Grade 11 module of ICCS, examining civic learning among older adolescents, particularly in vocational education pathways. You will engage in international comparative research, applying advanced
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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expertise in a supportive and innovative environment. In this role, you will lead the computational strand of the project, applying molecular simulations, data analysis, and machine learning to uncover how
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researchers on 5G testbed operation, integration and performance testing. Job Requirements Bachelor’s, master's or Ph.D. in Electrical and Computer Engineering, Computer Science or related field. Solid
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our education, while benefitting them with our talent supply and collaborative research achievements. The University’s unique applied learning pedagogy integrates work and study, embedding authentic
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- Salary Sacrifice Car Scheme (Tusker) - Salary Sacrifice Cycle Scheme - Salary Sacrifice Home & Tech Scheme - Life Assurance Full details on our employee benefits can be found on our careers page
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to writing bids Operating within the area of gravitational-waves and astrophysics Applying probabilistic inference to gravitational waves, including through machine learning techniques Modelling