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analysis in biomedical data, in affiliation to the Artificial Intelligence Research Centre . The successful candidate will develop statistical and machine learning techniques to analyse biomedical data. High
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will be to: Research statistical methods and industrial production theory to develop a generic approach to the problem. Compile a comprehensive list of manufacturing parameters, material properties and
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Attend UCL courses and training (e.g. in research study design, good clinical practice, medical statistics, time management, paper writing). Literature research Manuscript writing for submission to peer
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costs of their project. We will support two students, one in the area of health data science/epidemiology/statistics and the other specialising in applied machine learning and informatics. The closing
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health (3) Support and training The doctoral student will be supported by a team with expertise in plant biology, soil health, next generation sequencing and statistics and they will work within an active
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distinction is desirable, but not essential. Strong statistical skills, and experience with designing and implementing experiments with human participants, are useful. Applicants will need to submit a CV, a
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, good clinical practice, medical statistics, time management, paper writing). Literature research Manuscript writing for submission to peer-reviewed journals) Attend weekly research meetings Other
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, brain imaging techniques, and advanced statistical analyses. The selected candidate will receive comprehensive supervision from a team of expert researchers and clinicians with diverse backgrounds
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on recent developments in computational polymorph prediction and nucleation modelling that benefit from synergies between statistical mechanics, machine learning and computational chemistry, and will develop
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in the same broad subject area (e.g. computer science, AI, data science), or in a core-STEM subject (e.g. physics, engineering, mathematics, statistics, IT) Detailed eligibility criteria Application