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behaviour change interventions. Your knowledge of health-related behaviour theories will be essential. You will have experience in recruitment, data collection, and thematic analysis, including focus group
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Engineering, Data Science and AI theory and practice, ensuring their co-evolution and competent adoption across disciplines to enable transformative, interdisciplinary, impactful data-intensive research
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Anniversary Fellowships - Global Talent Section: Research Strategy & Funding Location: Highfield Campus Salary: Lecturer (Level 5 £45,413-£57,422), Associate Professor (Level 6 £59,139-£73,818), Professor (Level 7 - Competitive: Salary will be commensurate with qualifications and...
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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publications in top journals (or conferences) in one or more of the following areas: Quantum machine learning, Quantum algorithms , Quantum information theory or Theoretical Physics Essential criteria
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Mathematics (STEM). Further details Job Description Where to apply Website https://www.timeshighereducation.com/unijobs/listing/398296/research-fellow-in-… Requirements Additional Information Work Location(s
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or applying evidence, theories and techniques of behaviour change. Experience in carrying out qualitative and quantitative behavioural science research. Excellent scientific writing skills, with a peer-reviewed
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Theory Group (SMTG) is led by Professor David O. Scanlon. We are seeking to recruit a Research Fellow in Computational Discovery of Wide Band Gap Conducting Oxides, who will computationally explore new
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the essential criteria for the post, which includes: Have or be about to obtain a PhD in computer science, engineering, mathematics or physical sciences area. Recent high quality research experience in
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excellence in Data Engineering, Data Science and AI theory and practice, ensuring their co-evolution and competent adoption across disciplines to enable transformative, interdisciplinary, impactful data