Research Associate in Computer Vision and Environmental Modelling

This position offers a Research Associate role at the University of Sheffield’s Computer Vision Lab (PRISMA Team) to advance automated forest inventory systems. The successful candidate will design and implement deep learning frameworks for tree species mapping in diverse forests, focusing on open-set recognition and uncertainty-aware models. The work spans data processing, algorithm development, and tool deployment (e.g., QGIS plugins) while collaborating with interdisciplinary teams in AI, remote sensing, ecology, and forest conservation. The project aims to address real-world challenges like illegal logging through cutting-edge computer vision and geospatial analysis. Ideal candidates will bridge AI research with practical environmental applications, contributing to both academic outputs and stakeholder-driven solutions.

• Develop and evaluate deep learning frameworks for spatio-spectral-temporal UAV imagery to improve tree species mapping in diverse forests.
• Investigate open-set recognition approaches to enable AI systems to identify known species while handling previously unseen classes.
• Explore foundation models and multimodal learning techniques to enhance robustness and adaptability of tree species mapping systems.
• Design uncertainty-aware and incremental learning methods to improve model performance in real-world, unstructured environments.
• Translate research into practical tools, including contributing to the development of a QGIS plugin for forest monitoring.
• Validate algorithms using UAV imagery and collaborate with interdisciplinary teams in AI, remote sensing, ecology, and forest conservation.
• Work across the research pipeline, from data processing and model development to validation and deployment of solutions.