2 PhD and 2 Postdoctoral Positions in Deep Learning

University of Bern - Computer Vision Group

Are you passionate about deep learning, self-supervised learning, generative AI, and pushing the boundaries of artificial intelligence? The Computer Vision Group (CVG) at the University of Bern, Switzerland, invites applications for two PhD positions and two postdoctoral positions across two ambitious research projects.

About the positions

Project 1: Collaborative World Models

1 postdoctoral position, joining two PhD researchers already recruited for the project

This project investigates collaborative world-model agents: independently operating visual models that learn to predict how environments evolve, retain distinct perspectives and memories, and communicate to solve problems together. The research combines generative video modeling, self-supervised learning, memory, test-time adaptation, and decision-making, with potential applications in embodied AI and robotics. The successful candidate will join an existing team working on controllable, memory-augmented, and task-solving world models.

Project 2: Self-Supervised and Meta-Learning for Rapid Adaptation

2 PhD positions and 1 postdoctoral position

This project investigates how the structure of pretraining data and the choice of learning objective shape the prior knowledge acquired by deep-learning models, and how these priors can be optimized to enable rapid adaptation to novel tasks. The work combines novel datasets, controlled benchmark environments, evaluation methods, and new learning algorithms and architectures. The project is fully funded for four years and is carried out in close collaboration with Prof. Andrea Vedaldi at the University of Oxford.

General information

• All positions are fully funded within their respective projects.
• Start date: October 1, 2026, or by agreement.
• Applications will be reviewed until excellent candidates are found.
• Successful candidates will conduct original research in a well-established and dynamic research group.

Your profile

PhD applicants

• A master’s degree in computer science, engineering, mathematics, or a related field, completed or expected by the starting date.
• A strong interest in fundamental research in machine learning and artificial intelligence.

Postdoctoral applicants

• A PhD in computer science, engineering, mathematics, or a related field, completed or expected by the starting date.
• A strong research and publication record in machine learning, deep learning, computer vision, or a closely related area.
• The ability to conduct independent research and contribute to the scientific guidance of junior researchers.

All applicants

• A solid foundation in machine learning, deep learning, and computer vision.
• Strong skills in applied mathematics, probability, and programming, such as Python or C/C++.
• Experience with at least one major deep-learning framework, preferably PyTorch.
• The ability to work both independently and collaboratively.
• Excellent communication skills and fluency in English.

Experience relevant to at least one project—including world models, video generation, generative modeling, self-supervised learning, meta-learning, reinforcement learning, multi-agent systems, or embodied AI—is an advantage. Applicants are not expected to have expertise in all these areas.

What we offer

• A collaborative and innovative research environment.
• Access to state-of-the-art computing infrastructure, including the Swiss AI large-scale GPU cluster.
• Opportunities to publish at top-tier conferences and participate in the international research community.
• Funding for international conferences, workshops, and training programs.
• A competitive salary according to University of Bern regulations, with additional compensation for teaching duties.
• The opportunity to live in Bern, a beautiful and highly livable city in the heart of Switzerland.

Interested?

Submit your application through this portal by choosing a project below and then selecting a PhD or postdoctoral application. Applicants may apply to both projects where appropriate. Applications submitted directly by email will not be considered.

Choose a project

Select a project to continue to its PhD or postdoctoral application.