Research Associate in Machine Learning for Astronomical Imaging (Fixed Term)
Conducts original research in machine learning for astronomical imaging, focusing on detecting and characterising low surface brightness structures in large-scale surveys. Develops and implements AI methods such as generative and simulation-based models, and builds scalable software infrastructure for data pipelines and model deployment on GPU/HPC systems. Publishes findings, presents at conferences, and contributes to open-source research software and student supervision.