| EPSRC Reference: |
EP/S022961/1 |
| Title: |
UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) |
| Principal Investigator: |
Shuckburgh, Dr E |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Earth Sciences |
| Organisation: |
University of Cambridge |
| Scheme: |
Centre for Doctoral Training |
| Starts: |
01 April 2019 |
Ends: |
30 September 2027 |
Value (£): |
6,953,400
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| Environment |
Information Technologies |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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07 Nov 2018
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UKRI Centres for Doctoral Training AI Interview Panel U – November 2018
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Announced
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Summary on Grant Application Form |
The UKRI Centre for Doctoral Training in "Application of Artificial Intelligence to the study of Environmental Risks" will develop a new generation of innovation leaders to tackle the challenges faced by societies across the globe living in the face of environmental risk, by developing new methods that exploit the potential of Artificial Intelligence (AI) approaches to the proper analysis of complex and diverse environmental data. It is made of multiple departments within Cambridge University, alongside the British Antarctic Survey and a wide range of partners in industry and policy. AI offers huge potential to transform our ability to understand, monitor and predict environmental risks, providing direct societal benefit as well as potential commercial opportunities. Delivering the UN 2030 Sustainable Development Agenda and COP 21 Paris Agreement present enormous and urgent challenges. Population and economic growth drive increased demands on a planet with finite resources; the planet's biodiversity is suffering increasing pressures. Simultaneously, humanity's vulnerabilities to geohazards are increasing, due to fragilities inherent in urbanisation in the face of risks such as floods, earthquake, and volcanic eruptions. Reliance on sophisticated technical infrastructures is a further exposure. Understanding, monitoring and predicting environmental risks is crucial to addressing these challenges. The CDT will provide the global knowledge leadership needed, by building partnership with leaders in industry, commerce, policy and academia in visionary, creative and cross-disciplinary teaching and research. Vast and growing datasets are now available that document our changing environment and associated risks. The application of AI techniques to these datasets has the potential to revolutionise our ability to build resilience to environmental hazards and manage environmental change. Harnessing the power of AI in this regard will support two of the four Grand Challenges identified in the UK's Industrial Strategy, namely, to put the UK at the forefront of the AI and data revolution and to maximise the advantages for UK industry from the global shift to clean growth.
The students in the CDT will be trained in a broad range of aspects of the application of AI to environmental risk in a multi- disciplinary and enthusing research setting, to become world-leaders in the arena. They will undertake media training activities, public engagement, and training in the delivery of policy advice as well as the development of entrepreneurial skills and an understanding of the approach of business to sustainability. Discussion of the broader societal, legal and ethical dimensions will be integral to this training. In this way the CDT will seed a new domain of AI application in the UK that will become a champion for the subject globally.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.cam.ac.uk |