This Earth Observation CDT is an exciting new centre funded by the Natural Environment Research Council and the UK Space Agency, that will train 50 PhD students to tackle cross-disciplinary environmental problems, by applying state-of-the-art data science methods to the deluge of satellite data collected each day. Our graduates will be supervised by a consortium of world-leading UK scientists, with topics co-developed with the UK’s most innovative spatial data companies. By training a new generation of industry-experienced satellite data specialists we will support the growing strategic importance of remote sensing within the UK space sector, and enhance the UK’s profile as an international leader in Earth Observation science.
It aims to create a:
- Internationally renowned centre for research training in Earth Observation and data science techniques, and their application to Earth system challenges.
- Bonded cohort of self-supporting students, with strong mentoring across years, sites and disciplines.
- Multi-disciplinary training environment with graduates that have the skills and ambition to innovate and become future Earth Observation leaders.
- Open and collaborative partnerships between academia and industry, which stimulates exploitation of satellite data and seeds new research ideas.
- Happy, diverse and inclusive research environment that leads the way on widening participation.
- New satellite datasets and techniques that will deliver a meaningful legacy to the UK through careers in industry, NGOs, government and academia.
The research conducted by these new NERC Earth Observation PhD studentships covers a range of thematic areas, including:
- Atmospheric science, composition, and meteorology
- Land and sea ice cryosphere research
- Monitoring the ocean temperature, sea state, dynamic ocean topography
- Land cover change
- Forestry, burnt area, and biomass
- EO data assimilation into climate models
- Combining Earth Observation datasets with advanced computer techniques, such as AI, neural networks, and machine learning
|University of Edinburgh |
Founded in 1583 and based in Scotland’s historic and beautiful capital the University of Edinburgh is globally recognised for our research, development and innovation as well as excellence of teaching at all levels. Sense is located within the College of Science and Engineering and based at the Kings Buildings campus.
|University of Leeds |
Established in 1904 and based in the dynamic and exciting Yorkshire city, the University of Leeds is one of the largest higher education institutions in the UK. It is renowned globally for the quality of its teaching and research and strives to achieve academic excellence within an ethical framework. Sense is located within the School of Earth and Environment which is located at the main city centre campus.
|National Oceanography Centre|
The National Oceanography Centre (NOC) is a marine science research and technology institution based on two sites in Southampton and Liverpool, United Kingdom. It is the UK’s largest institution for integrated sea level science, coastal and deep ocean research and technology development.
The centre was set up to work in close partnership with institutions across the UK marine science community to address key science challenges, including sea level change, the oceans’ role in climate change, predicting and simulating the behaviour of the oceans through computer modelling, the future of the Arctic Ocean and long-term monitoring technologies.
|British Antarctic Survey |
The British Antarctic Survey (BAS) is the United Kingdom’s national Antarctic operation. It is part of the Natural Environment Research Council. With over 400 staff, BAS takes an active role in Antarctic affairs, operating five research stations, two ships and five aircraft in both polar regions, as well as addressing key global and regional issues. This involves joint research projects with over 40 UK universities and more than 120 national and international collaborations. The headquarters of the BAS are in the university city of Cambridge