Building community climate resilience with large language models and remote sensing (Ref: SCI25-ND)

Communities are often highly motivated to contribute to solutions that enhance their resilience to the increasing effects of climate change. However, while much knowledge already exists on resilient infrastructure, land use and restoration schemes, early warning systems, recovery and reconstruction practices, amongst others, one-size-fits-all solutions do not account for regional disparities, individual community needs and capabilities.

There are three barriers to the planning and implementation of concrete, actionable and tailored resilience strategies: (1) access to relevant place-dependent knowledge, e.g. what could work here?; (2) understanding of the practical steps involved in implementing the identified solutions, e.g. who to involve?, how to fund?, what to consider?; and (3) enabling coordination of isolated local projects to converge into coherent joint action at a larger level.

Our project will develop a decision support tool that helps communities create tailored action plans towards climate resilience and adaptability for their local community.

Large language models will serve to identify, extract and interpret knowledge from existing document collections, such as scientific articles, climate reports, environmental impact assessments, and policy documents, that can translate into a reliable knowledge base. Remote sensing data will be used to enhance information gained from text, for example, to gather information on geographical features, land use, potential geohazards, to inform which solutions work in specific places and contexts.

The successful candidate will work in the new Loughborough Language & Data research group in Computer Science and will also collaborate with colleagues at the UK’s Centre for Environment, Fisheries and Aquaculture Science (CEFAS) towards pathways of impact.

Supervisor:

• Nina Dethlefs

Entry requirements:

Students should have at least, or expected to achieve, a 2:1 honours degree (or equivalent international qualification) or equivalent experience in an area related to computer science.

English language requirements:

Applicants must meet the minimum English language requirements. Further details are available on the International website.

Funding information:

The studentship is for 3 years full-time equivalent and provides a tax-free stipend of £19,237 per annum for the duration of the studentship plus university tuition fees.

Please note that studentships will be awarded on a competitive basis to applicants who have applied to this project and other advertised projects starting with advert reference ‘SCI25-’ in the School of Science. Successful candidates will be informed during April 2025.

How to Apply:

All applications should be made online. Under programme name, select ‘Computer Science’. Please quote the advertised reference number: ‘SCI25-ND’ in your application. 

To avoid delays in processing your application, please ensure that you submit a CV and the minimum supporting documents. 

The following selection criteria will be used by academic schools to help them make a decision on your application.

Project search terms:

artificial intelligence, environmental sciences, remote sensing, large language models, natural language processing, resilience

Email Address:

sci-pgr@lboro.ac.uk