**Job Description**
We are seeking a motivated PhD candidate to develop innovative artificial intelligence methods for plant species recognition from aerial imagery in tropical forests. This research will investigate the long-tailed open-ended semantic segmentation problem and advance new approaches for uncertainty estimation and confidence-aware predictions. The successful candidate will join an international, interdisciplinary team, contributing to AI solutions with direct impact on biodiversity monitoring, conservation planning, and sustainable forest management. Tropical forests, rich in biodiversity, pose significant challenges for plant species monitoring and mapping due to their complexity, prevalence of rare species, and the emergence of previously unseen classes. This PhD project will specifically investigate Long-Tailed Open-Ended Semantic Segmentation (LTOESS), a critical research frontier in computer vision, combining class imbalance, recognition of rare/unseen species, and dense labelling of high-resolution imagery. The candidate will develop novel approaches for uncertainty estimation and confidence-aware predictions to ensure trustworthy results for complex forest environments.
**Skills & Abilities**
• Strong programming skills (Python preferred)
• Solid understanding of machine learning and deep learning, including computer vision techniques
• Ability to read, write, and communicate scientific texts clearly
• Strong analytical and problem-solving skills
• IELTS score of 6.5 overall, with no less than 6.0 in each component (if English is not first language)
• Experience with remote sensing or geospatial tools (e.g., QGIS) (Desirable)
• Previous experience publishing papers (e.g., from MSc/MRes/MEng projects) (Desirable)
• Interest or experience in ecology, biodiversity, or environmental monitoring (Desirable)
**Qualifications**
Required Degree(s) in:
• Computer Science
• Physics
• Mathematics
• Engineering
• Related field
**Experience**
Other:
• A good Bachelor’s (Hons) and/or Master’s degree required
• Applicants from other backgrounds with strong quantitative, programming, or analytical experience are also encouraged to apply.
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