Research Associate in Computer Vision and Environmental Modelling

Research Associate in Computer Vision and Environmental Modelling

• Working at Sheffield

Job Title:

Research Associate in Computer Vision and Environmental Modelling

Posting

Start Date:

22/09/2026

Job

School/Department:
Computer Science

Work Arrangement:
Full Time (Hybrid)

Contract Type:
Fixed-term

Salary per annum (£): £38,784 - £41,064

Closing Date: 18/10/2026

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 (Next Gen-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 Next Gen-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…