PhD Candidate in Fine-Grained Visual Understanding

Research focus

The PhD will address open research questions in Computer Vision related to fine-grained visual understanding and anomaly detection under limited, weak, or imperfect supervision. The precise research direction will be defined together with the candidate and may evolve over the course of the PhD.

Example directions include:
• Anomaly, novelty, and out-of-distribution detection in visual data
• Fine-grained visual understanding for distinguishing subtle irregularities
• Learning visual representations with limited, weak, or noisy supervision
• Adapting, specializing, or probing large pre-trained models for domain-specific visual understanding
• Self-supervised and representation learning for images and videos
• Robustness and generalisation in real-world vision systems
• Interpretability and explainability of anomaly detection and visual decision-making

As a candidate, you must have a strong background in machine learning and computer vision, as well as excellent programming skills. In addition, you must of course have an understanding and an interest in real-world applications.