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AI for the study of Environmental Risks (AI4ER)

UKRI Centre for Doctoral Training

The UKRI Centre for Doctoral Training 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 critical global environmental challenges by exploiting vast, diverse and often currently untapped environmental data sets. Embedded in the outstanding research environments of the University of Cambridge and the British Antarctic Survey (BAS), the AI4ER CDT addresses problems that are relevant to building resilience to environmental hazards and managing environmental change. The primary application areas are: Weather, Climate and Air Quality, Natural Hazards, Natural Resources (food, water & resource security and biodiversity).

The AI4ER CDT Research Experience Placement (REP) scheme aims to encourage suitably qualified undergraduate students to consider a career in artificial intelligence applied to environmental risk. 

We encourage that placements are undertaken in person, however this can be flexible depending on student and supervisor’s circumstances.

REP placements will be between 6-8 weeks and each student will be paid a stipend equivalent to the Real Living Wage (currently £10.90 per hour), paid monthly, based on 35 hour week.

 A list of available projects will be published shortly.


 Applicants must meet all of the following criteria to be eligible for this placement:

  1. Be studying for an undergraduate degree (or integrated Masters degree) in one of the following subjects: natural sciences (e.g. physics, chemistry, earth sciences, biology), engineering, computer science, mathematics. (The degree course should continue beyond summer 2023, i.e. students should not currently be in their final year. Priority will be given to students who will complete their course in 2024.)
  2. Be expected to obtain a first or upper second class UK honours degree.
  3. Be eligible for subsequent UKRI PhD funding and have the right to work in the UK.
  4. Meet the particular requirements (academic background/skills) associated with the project or projects of interest.

 How to apply 

 Students should submit their application by email to the CDT administrator (

  1. Single sheet giving: (a) Full Name, (b) DOB, (c) email, (d) Home address, (e) Nationality including confirmation of right to work in UK, (f) current academic course and expected graduation date, (g) List of projects for which you wish to apply, in priority order (most preferred first), (h) Name and contact details of referee (we will contact referee but applicants must have informed referee in advance of application).
  2. CV (no more than 2 pages).
  3.  Brief covering letter giving motivation for application – maximum 1 page.

 Closing Date: TBC  

What is expected of you

If you apply and are successful, we will expect you to join the University for 6-8 weeks on a full-time basis, and to participate fully in the Research Experience Placements programme. This will include:

  • fulfilling the project requirements;
  • attending all training and development sessions offered as part of the programme;
  • engaging with the other Research Experience Placements students and working as a team;
  • providing feedback at the time;
  • providing feedback after the scheme;
  • keeping in touch with supervisors and with the programme to let us know how your internship progresses;
  • following codes of conduct in the Department that hosts you;
  • submit a report after the placement ends. 

Previous Year Interns

Testimonials from interns

''Brilliant placement with a lot of opportunities to learn and develop, as well as gain a better understanding of a professional workspace.''

''It was very rewarding, given the flexibility and systematic agenda of the placement. The experience is one of the best I have ever had.''

''I greatly enjoyed what I did and learnt! (...) I found the transition from being an undergraduate learning in a structured way to having more independence trying to teach myself things being a super useful experience and would definitely recommend to others! I really liked the sense of freedom with this project to explore the topic.''

''Easy to access & very rewarding.''

Previous Years Projects



Title  Bayesian citizen science for STEM students at the Lake Tana Biosphere Reserve, Ethiopia  
Supervisor  Prof Alan Blackwell and Dr Tesfa Tegegne 
Department/Institution  Computer Science and Technology / Bahir Dar Institute of Technology and STEM Centre 


Title  Can machine learning link ocean data with the polar vortex? Using unsupervised classification of ocean data to track changes in Southern Hemisphere winds.
Supervisor  Emma Boland and Dan Jones 
Department/Institution  British Antarctic Survey 


Title  Using machine learning to explore options for replacing lead in lead perovskite solar cell
Supervisor  Bingqing Cheng 
Department/Institution  Department of Computer Science and Technology 


Title  Can Federated Learning Save the Planet? 
Supervisor  Nicholas Lane and Pedro Porto Buarque de Gusmão 
Department/Institution  Department of Computer Science and Technology 


Title  Assessing the effects of forestry on natural climate solutions 
Supervisor  Dr Andrew Tanentzap and Dr Jeremy Fonvielle 
Department/Institution  Plant Sciences / Conservation Research Institute 


Title  Monitoring changes in tropical forest carbon storage at the individual tree level. 
Supervisor  Dr Toby Jackson 
Department/Institution  Plant Sciences / Cambridge Conservation Initiative 


More details here.


Title  Deciphering hidden patterns in carbon cycling from molecules to landscapes 
Supervisor  Prof Andrew Tanentzap, Dr Lucas Braga and Dr Jeremy Fonvielle 
Department/Institution  Plant Sciences / Conservation Research Institute 


Title  Advancing volcanic ash forecast capabilities using machine learning
Supervisor  Thomas Aubry & Abhirup Ghosh
Department/Institution  Department of Geography& Department of Computer Science and Technology


Title  Mineralogical Analysis of Particular Matter (PM) Air pollution Particles using Machine Learning 
Supervisor  Po-Yen Tung, Richard Harrison 
Department/Institution  Earth Sciences 


Title  Automated detection of walrus haulouts to assess seasonality 
Supervisor  Ellen Bowler and Hannah Cubaynes 
Department/Institution  British Antarctic Survey 


Title  IceNet2 Operational Forecast refinement
Supervisor  Dr. Scott Hosking, James Byrne, Tom Andersson
Department/Institution  British Antarctic Survey Artificial Intelligence Lab


Title  Learning from high resolution 2017 hurricane damage data 
Supervisor  Dominic Orchard 
Department/Institution  Computer Lab