The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.
We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more. Find out more about our benefits (opens in a new window) and join us to become part of something special.
Overview
Applications are invited for a Research Associate to join the Next-Generation Forest Inventory (NextGen-FI) project, developing advanced AI and remote sensing methods for forest monitoring and illegal logging detection. Working at the intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and stakeholder partners.
The project addresses automated tree species mapping in highly diverse forests where real-world conditions and species may not be represented in initial training data. Research will focus on open-set recognition, foundation models, multimodal approaches, and uncertainty-aware, incremental learning to build adaptable AI systems that recognise known species while handling unseen classes. The role spans the entire research pipeline – from processing spatio-spectral-temporal UAV imagery to model development, algorithm validation and deployment, including contributions to a QGIS forest monitoring plugin.
Based in the PRISMA Computer Vision lab within the School of Computer Science at the University of Sheffield, the post offers access to high-performance computing and GPU facilities. Applicants should hold a PhD in computer science or a related discipline. Candidates from GIS, remote sensing, or ecology backgrounds are also encouraged to apply, particularly those with practical experience in UAV data processing, machine learning, or computer vision.
Main Duties And Responsibilities
Undertake research within Work Packages 2, 3 and 4 of the NextGen-FI project, contributing to the development of AI-enabled approaches for forest inventory and monitoring.
Develop and evaluate machine learning and deep learning methods for tree species mapping using UAV-based spatio-spectral-temporal imagery, including foundation models and multimodal learning approaches.
Investigate open-set and open-world recognition methods to address long-tailed species distributions and enable the identification and appropriate handling of previously unseen tree species.
Develop uncertainty-aware and incremental/continual learning approaches to improve the robustness, reliability and adaptability of tree species mapping systems.
Process and analyse UAV and geospatial data and conduct validation experiments, assessing the performance of developed algorithms using real-world datasets.
Contribute to the development of practical software tools, including a QGIS plugin for forest monitoring and the integration of research outputs into geospatial workflows.
Collaborate with academic and stakeholder partners and disseminate research findings through project deliverables, technical reports, academic publications, conferences and other appropriate channels.
Carry out other duties, commensurate with the grade and remit of the post.
Person Specification
Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.
Criteria
Essential Or Desirable
Stage(s) assessed at
A PhD in computer science, remote sensing, ecology or a related discipline
Essential
Application
Research experience in developing machine learning and/or deep learning methods.
Essential
Application/interview
Programming experience in Python and use of relevant machine learning / deep learning frameworks.
Essential
Application/interview
Experience of working with image, remote sensing or other spatial data.
Essential
Application/interview
Experience of applying machine learning methods to real-world datasets and interpreting their results.
Essential
Application/interview
Evidence of research outputs, such as peer-reviewed publications or conference papers, appropriate to career stage.
Essential
Application
Ability to communicate research findings effectively in written and oral form
Essential
Application/interview
Ability to work independently and collaboratively within a multidisciplinary research team, including with academic and stakeholder partners.
Essential
Application/interview
Experience with UAV/RPAS or multispectral imagery, and/or GIS software such as QGIS
Desirable
Application/interview
Knowledge of open-set recognition, long-tailed learning, uncertainty-aware learning, incremental learning or foundation models.
Desirable
Application/interview
Further Information
Grade
Grade 7
Salary
£38,784 - £41,064
Work arrangement
Full-time
Duration
1st February 2027 to 31st July 2028
Line manager
Lecturer in Computer Vision (project lead)
Direct reports
None
Right to work in the UK
If you do not currently hold the right to work in the UK, you can find more information here to help determine your visa eligibility. Additional guidance is also available on the UK Visa & Immigration website.
Our website
https://sheffield.ac.uk/cs
For informal enquiries about this job, contact Dr Jefersson A dos Santos, project lead, at
[email protected]
Next steps in the recruitment process
It is anticipated that the selection process will take place in early November. This will consist of an interview. We plan to let candidates know if they have progressed to the selection stage the week commencing 26th October. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process, you can contact
[email protected]
Our vision and strategic plan
We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).
What We Offer
A minimum of 41 days annual leave, including bank holiday and closure days (pro rata) with the ability to purchase more.
Flexible working opportunities, including hybrid working for some roles.
Generous pension scheme.
A wide range of discounts and rewards on shopping, eating out and travel.
A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
Recognition Awards to reward staff who go above and beyond in their role.
A commitment to your development access to learning and mentoring schemes; integrated with our Academic Career Pathways.
A range of generous family-friendly policies
paid time off for parenting and caring emergencies
access to menopause support in the workplace
paid time off and support for fertility treatment
and more
More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).
We are a Disability Confident Leader (opens in a new window). If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.
Closing Date : 18/10/2026
We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision (www.sheffield.ac.uk/vision) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.