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Research Fellow in Remote Sensing and AI

Cranfield University -
Cranfield

Apply Now

Job details

Full-time | Fixed term contract
£38,655 - £46,492 a year
8 days ago

Qualifications

Geography
Doctoral degree
Computer vision
Research
Analysis skills
Deep learning
Environmental monitoring
AI

Full job description

Research Fellow in Remote Sensing and AI

Organisation

Cranfield University

Faculty or Department

Faculty of Engineering and Applied Sciences

Based at

Cranfield Campus, Cranfield, Bedfordshire

Hours of work

37 hours per week, normally worked Monday to Friday. Flexible working will be considered.

Contract type

Fixed term contract

Fixed Term Period

Fixed Term Contract for 18 months, or until project end date (whichever is sooner)

Salary

Full time starting salary is normally in the range of £38,655 to £46,492 per annum

Posted Date

20/08/2026

Apply by

17/09/2026

Role Description

We welcome applications from passionate, talented, and dedicated individuals to help develop the next generation of AI-powered remote sensing technologies for monitoring landscape change and environmental sustainability at scale.

About the Role

We are seeking an ambitious Postdoctoral Researcher to join the Landscape Observatory at Cranfield, an exciting research initiative developing advanced Remote Sensing and Artificial Intelligence capabilities for environmental monitoring. You will develop and apply state-of-the-art deep learning methods for land cover classification and change detection using multi-source aerial and satellite imagery. Working within an interdisciplinary team and supported by cutting-edge HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific conferences.

About You

You will be educated to doctoral level in Remote Sensing, Geography, Geoinformatics, Computer Science, Artificial Intelligence, Machine Learning or a related discipline. You will have experience developing deep learning models using frameworks using PyTorch and possess strong Python programming skills. Experience working with Remote Sensing datasets and geospatial technologies such as GDAL or RioXarray is essential. You should have knowledge of semantic segmentation, time-series analysis and modern AI approaches together with a developing publication record appropriate to your career stage.

Experience with High-Performance Computing (HPC) clusters, geospatial foundation models, large-scale geospatial processing or cloud-based Earth Observation platforms would be advantageous.

You will be a highly motivated researcher with strong analytical and problem-solving skills, excellent communication abilities and a passion for delivering impactful environmental research.

About Us

As a specialist postgraduate university, Cranfield’s world-class expertise, large-scale facilities and unrivalled industry partnerships are creating leaders in technology and management globally. Learn more about Cranfield and our unique impact here.

The role will be based within Cranfield Environment Centre, which undertakes internationally recognised research focused on understanding and managing environmental change through the integration of environmental science, remote sensing, artificial intelligence, systems modelling and data science. Through initiatives such as the Landscape Observatory, the Centre develops innovative solutions that support environmental monitoring and decision-making at scale. Further information is available at: Cranfield Environment Centre.

Our Values and Commitments

Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here.

We aim to create and maintain a culture in which everyone can work and study together and realise their full potential. We are a Disability Confident Employer. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly employers in the UK by the charity Working Families. Find out more about our key commitments to Equality, Diversity and Inclusion and Flexible Working here.

Working Arrangements

Collaborating and connecting are integral to so much of what we do. Our Working Arrangements Framework provides many staff with the opportunity to flexibly combine on-site and remote working, where job roles allow, balancing the needs of our community of staff, students, clients and partners.

How to apply

For an informal discussion about this opportunity, please contact Dr Daniel Simms, Senior Lecturer in Remote Sensing, on (E): [email protected]

Please do not hesitate to contact us for further details on E: [email protected]. Please quote reference number 5381.

Closing date for receipt of applications: 17 September 2026

Please note that we reserve the right to close this advert prior to the stated closing date should we receive sufficient numbers of applications. Therefore, we would encourage you to complete and submit your application as soon as possible.

Apply Now

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