| | MRes + PhD in Application of Artificial Intelligence to the study of Environmental Risks | | |
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| School | University of Cambridge Postgraduate Study | | |
| Location | Cambridge, EGL, United Kingdom | | |
| School Type | Graduate School | | |
| School Size | Full-time Undergraduate: 12,850
Full-time Graduate: 11,600 | | |
| Degree | Doctorate | | |
| Honours | | | |
| Co-op | | | |
| Length | 4 Year(s) | | |
| Entry Grade (%)* | | | |
| Prerequisites | | | |
| Prerequisites Notes | Bachelor degree (Honours) or 4 years Bachelor's without Honours or Baccalaureat / Bachelier (first-cycle degrees in Quebec province (3 years) (except McGill University)) or Bachelor degree (Honours) or Bachelor's without Honours (3-4 years with 120 credits) from McGill University or First Professional Degree / Grade Professionnelle (titles include Doctor of Dental Medicine / Surgery, Doctor of Medicine and Juris Doctor) with a grade of 3.3/4, 3.3/4.3, B+, 7/9 (York University) | | |
| Cost |
Tuition cost is converted from £33,972. | | |
| Scholarships | | | |
| Description | The UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) trains researchers to be uniquely equipped to develop and apply leading-edge computational approaches to address critical global environmental challenges by exploiting vast, diverse and often currently untapped environmental data sets. Students will engage in a one-year MRes degree in Physical Sciences (Environmental Data Science) which includes a taught component and a major research element. On successful completion of the MRes, a three-year PhD research project will be undertaken.
Students will receive training in research, professional, technical and transferable skills through a focused core programme with an emphasis on the development of data science skills. The overall objectives of the programme are to: provide students with a broad understanding of the range of urgent environmental challenges facing global society and the practical experience of applying AI-based tools to address these challenges; build a cohort of students and equip them with skills that prepare them optimally for PhD research; develop entrepreneurial and project management skills and generate awareness of industrial, commercial and policy drivers through relevant cohort activities; and equip students with a range of skills to enable them to take prominent roles in a wide spectrum of employment and research after the PhD. | | |
| Next Steps | | | |