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University of Cambridge: Application of Artificial Intelligence to the study of Environmental Risks

Institution University of Cambridge
Department Computer Laboratory
Study type Research


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. Students will undertake both individual masters-level research projects, as well as a guided team challenge, before embarking on their PhD research. Develop entrepreneurial and project-management skills and generate awareness of industrial, commercial and policy drivers through relevant cohort activities and close integration of CDT partners in the delivery of the educational programme. Learning Outcomes By the end of the programme, students will have:

Learnt additional skills in disciplines outside of their first degree; Gained understanding and command of methods and techniques relevant for research at the interface between artificial intelligence and machine learning on the one hand and the study of environmental change and risk on the other; Attended lectures in degree-level topics bespoke to complement their own strengths and knowledge base upon entry, gaining a broad overview and specific knowledge of environmental data science, shared across the whole cohort; Developed skills in research methods through the execution of a masters level independent research project; Developed a full interdisciplinary PhD proposal they can defend in an oral examination and, if successful, embark on from their 2nd year at the CDT; Gained an understanding of the Enterprise landscape relating to environmental data science; Developed a good transferrable skills base, including science communication skills, as well as a sound grasp of safety and ethics in research; Learnt to work effectively in teams as well as individually.

PhD with Integrated Study

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