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AI for the study of Environmental Risks (AI4ER)

AI for the study of Environmental Risks (AI4ER)

  • Home
  • CDT Directors
  • People
  • AI4ER Partners

About AI4ER

  • About AI4ER overview
  • Find us
  • Applying to us
  • MRes (Year 1)
  • PhD (Years 2-4)

Events

  • Events overview
  • CDT Showcase 2026

Research

  • Research overview
  • Publications

Space, oceans and polar science

  • Space, oceans and polar science overview
  • Space, oceans and polar science - staff
  • Space, oceans and polar science - students

Built environment and energy

  • Built environment and energy overview
  • Built environment and energy - students
  • Built environment and energy - staff

Carbon capture and carbon credits

  • Carbon capture and carbon credits overview
  • Carbon capture and carbon credits - staff
  • Carbon capture and carbon credits - students

Climate and air quality

  • Climate and air quality overview
  • Climate and air quality - staff
  • Climate and air quality - students

Ecology and biodiversity

  • Ecology and biodiversity overview
  • Ecology and biodiversity - staff
  • Ecology and biodiversity - students

Geophysical and environmental modelling

  • Geophysical and environmental modelling overview
  • Geophysical and environmental modelling - staff
  • Geophysical and environmental modelling - students

Natural resources and forests

  • Natural resources and forests overview
  • Natural resources and forests - staff
  • Natural resources and forests - students

Training

  • Training overview
  • Placements
  • Training and other useful resources
  • Wellbeing resources

Equality and Diversity

  • Equality and Diversity overview
  • Student Support
  • ED&I training
  • Recruitment and admissions
  • Feedback and annual survey
  • ED&I Committee
  • ED&I useful resources and links
  • Home
  • CDT Directors
  • About AI4ER

    About AI4ER

    About AI4ER overview
    • Find us
    • Applying to us
    • MRes (Year 1)
    • PhD (Years 2-4)
  • Events

    Events

    Events overview
    • CDT Showcase 2026
  • People
  • Research

    Research

    Research overview
    • Space, oceans and polar science
    • Built environment and energy
    • Carbon capture and carbon credits
    • Climate and air quality
    • Ecology and biodiversity
    • Geophysical and environmental modelling
    • Natural resources and forests
    • Publications
  • Training

    Training

    Training overview
    • Placements
    • Training and other useful resources
    • Wellbeing resources
  • AI4ER Partners
  • Equality and Diversity

    Equality and Diversity

    Equality and Diversity overview
    • Student Support
    • ED&I training
    • Recruitment and admissions
    • Feedback and annual survey
    • ED&I Committee
    • ED&I useful resources and links
    • Home
    • CDT Directors
    • About AI4ER
    • Events
    • People
    • Research
    • Training
    • AI4ER Partners
    • Equality and Diversity

AI for the study of Environmental Risks (AI4ER)

The UKRI Centre for Doctoral Training (CDT) in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) trains researchers to develop and apply leading edge computational approaches to address the critical environmental challenges that our planet is currently facing.

Events

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AI for the study of Environmental Risks (AI4ER) CDT Showcase 2026

19 May 2026 11:30am to 5:30pm AI4ER CDT Showcase 2026

Research Themes

Climate and Air Quality - image by Brian McGowan, Unsplash
Climate and Air Quality

Find out more about the specific research topics that our AI4ER staff and students are undertaking in this field

Built Environment and Energy - image by Nicholas Doherty, Unsplash
Built Environment and Energy

Find out more about the specific research topics that our AI4ER staff and students are undertaking in this field.

Space, Oceans and Polar Science - image by Jeremy Bishop, Unsplash
Space, oceans and polar science

Find out more about the specific research topics that our AI4ER staff and students are undertaking in this field

Carbon capture
Carbon capture and carbon credits

Carbon capture will play a pivotal role in reducing the planet’s overall carbon emissions. AI and machine learning techniques are now being successfully implemented in these fields, from molecular to operational levels to improve processes and to develop state of the art technologies.

AI for the study of Environmental Risks (AI4ER)

Contact Information

Department of Earth Sciences Madingley Rise House, Madingley Road Cambridge CB3 0EZ
ai4er@esc.cam.ac.uk

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