The University Hospital Bonn (UKB) is one of Germany’s leading university hospitals, combining top-level performance in medicine and research withexcellence in teaching. Each year, more than half a million patients receive inpatient and outpatient care at the UKB. Around 3,500 students study medicine and dentistry here, and over 600 people are trained annually in healthcare professions. With approximately 9,900 employees, the UKB is the third-largest employer in the Bonn/Rhein-Sieg region. In the Focus hospital ranking, the UKB ranksfirst among universityhospitalsin North Rhine-Westphalia andsecond nationwide in terms of case mix index (case severity). In 2024, the UKB raised nearly €100 million in third-party funding for research, development, and teaching. The F.A.Z. Institute has named the UKB “Germany’s Training Champion” and “Germany’s Most Attractive Employer” for the fourth consecutive year.

The following part-time position (25.0 hours/week) is to be filled as of 01 April 2026 at the Eye Clinic and the Department of Computer Science at the University Hospital Bonn.:

PhD Position in the Field of Artificial Intelligence (m/f/d)

The position is fixed-term until 31 March 2028.

As part of a close and successful collaboration between the Eye Clinic and the Department of Computer Science at the University of Bonn, we are developing an AI-based system that analyzes video recordings of eye surgeries produced for training purposes. The initial goal is to provide automated annotations of such videos to medical students, in order to del iver individual feedback on compliance with best practices, instrument handling, and potential complications. In a second step, a system for real-time feedback during surgical training will be constructed.

Tasks:

• Develop computer vision models to analyze surgical videos, including action recognition, instrument usage, and relevant interactions
• Build and annotate a suitable dataset in close collaboration with other project staff; develop annotation and quality-assurance tools
• Evaluate the models together with teachers and students; validate learning progress
• Prototype the implementation of a Teaching Assistant
• Publish results in journals and at conferences; contribute to the design of teaching and training offerings

Requirements:

• MSc or an equivalent degree in Computer Science or related fields with a strong background in machine learning and computer vision
• Solid knowledge of Python and deep learning frameworks (PyTorch); experience with modern architectures for video analysis, temporal modeling, and/or real-time systems is advantageous
• Enthusiasm for solving medical challenges, ideally with prior experience working with medical data
• Ability to work independently and in multidisciplinary teams
• Very good command of German or English

We offer:

• Responsible and diverse: A position with plenty of room for creativity and responsibility within a supportive and collegial team
• Fair compensation: Pay according to the TV-L collective agreement, including annual bonus and company pension scheme
• Family-friendly flexibility: Flexible working time models, access to a place in our company daycare center, and programs supporting employees returning from parental leave
• Smart commuting: Excellent connection to the SWB public transport network and the option of an interest-free loan for the purchase of an e-bike
• Supported development: Individual training opportunities through courses, e-learning programs, and coaching sessions
• Structured onboarding: A systematic introduction to your new role and team
• Healthy at work: A wide range of health and wellness programs, plus free membership at the UKB fitness studio
• Employee benefits: Access to discounted offers for employees (corporate benefits)

We are committed to diversity and equal opportunities. Our goal is to increase the proportion of women in areas where women are underrepresented and to particularly promote their careers. We therefore urge relevantly qualified women to apply. Applications are treated in accordance with the State Equal Opportunities Act. Applications from suitable people with proven severe disabilities and those of equal status are particularly welcome.

How to apply:

Do you meet our requirements and are you looking for a varied and challenging position? If so, please do not hesitate to send your detailed application (preferably by email as a single file of up to 5 MB) by 31 March 2026, quoting the job advertisement number ST-38031, to: