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- https://www.kcl.ac.uk/jobs/088690-yeoh-tiong-lay-early-career-fellowship
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disease. This will involve data-driven feature extraction and the training and optimization of various machine learning models. The research will focus on analysing data from deeply phenotyped cohorts
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About the Role We are looking for a Research Fellow to join our NTU-GridLab team to work on advanced machine learning techniques in the area of energy systems with a specific focus on local energy
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Management Architecture – Platform for aircraft (machine learning based algorithms could be employed to process the data provided by such approaches). You will also be responsible for managing the research
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), through the development and application of a digitalized Health Management Architecture – Platform for aircraft (machine learning based algorithms could be employed to process the data provided by
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analysis, medical image computing, and machine learning. While previous experience in these areas is advantageous, it is not essential. Familiarity with magnetic resonance imaging (MRI) is preferred
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machine learning for functional materials design. You should have a PhD in Computer Science, Chemistry, Physics, Materials Science, or a related discipline, with a commitment to developing collaborative
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also open to discussing flexible working arrangements. Do you have research expertise in Machine Learning with a specific focus on analysing human decisions and behaviour? Are you interested in
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transcriptomic profile at neurodegenerative disease loci have limited our understanding of disease pathogenesis and hold key information on the causes and disease modifiers of neurodegeneration. This PhD
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where appropriate. About You PhD in the broad area of Computer Science/Engineering and/or Applied Mathematics Detailed and highly specialised knowledge and understanding of Computer Vision, Deep Learning
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View All Vacancies Engineering Location: Jubilee Campus Salary: £30,487 to £45,585 per annum (pro rata if applicable), depending on skills and experience (minimum £33966 with relevant PhD). Salary