7 machine-learning-phd "University of Exeter ;" scholarships

PhD Studentship: IMPACT-RISE: Infrastructural surrogate Modelling using Physics-informed And interpretable machine learning for CommuniTy ResIliency and Sustainability Evaluation

Supervisory Team Dr Jawad Fayaz, Department of Computer Science, University of Exeter Prof Edward Keedwell, Department of Computer Science, University of Exeter Project Description The IMPACT-RISE

Lecturer (Education and Scholarship) in Genomic Medicine

or depth of specialist and core knowledge in the discipline, demonstrated by a PhD (or nearing completion) or equivalent in genetics to develop teaching programmes, and teach and support learning; Use a

Lecturer in Medieval History (Education and Scholarship)

knowledge in the discipline, demonstrated by a PhD or equivalent in Medieval History to develop teaching programmes, and teach and support learning Use a range of delivery techniques to enthuse and engage

Lecturer in Law (Education and Scholarship)

(up to 26wks full pay), paternity leave (up to 6wks full pay), and a new Fertility Treatment Policy; and a beautiful campus set in the heart of stunning Cornwall The University of Exeter We are a member

Lecturer in Modern British History (Education and Scholarship)

, demonstrated by a PhD or equivalent in Modern British History to develop teaching programmes, and teach and support learning Use a range of delivery techniques to enthuse and engage students Participate in and

Lecturer in Classics and Ancient History (Education and Scholarship)

, demonstrated by a PhD or equivalent in Classics and Ancient History, to develop teaching programmes, and teach and support learning on ancient history modules Use a range of delivery techniques to enthuse and

PhD Studentship: NIHR Exeter BRC PhD Studentship - Investigating Predictors of Chronification of Post-surgical Pain using Data Mining and Qualitative Interviews: An International Mixed

validate established candidate risk factors in independent datasets and identify additional candidates using traditional statistics and machine learning. Subproject 2: Leading qualitative interviews with