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Andrea Vedaldi

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2 PhD and 2 Postdoctoral Positions in Deep Learning University of Bern in Switzerland

Degree Level

PhD, Postdoc

Field of study

Computer Science

Funding

Full funding available

Deadline

Oct 1, 2026

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Country

Switzerland

University

University of Bern

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Keywords

Computer Science
Electrical Engineering
Deep Learning
Mathematics
Computer Vision
Reinforcement Learning
Self-supervised Learning
Robotics
Statistics
Applied Mathematic
Multi-agent System
Machine learning

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About this position

The Computer Vision Group (CVG) at the University of Bern in Switzerland is inviting applications for two PhD positions and two postdoctoral positions in deep learning and artificial intelligence. The openings are spread across two ambitious research projects and are suited to candidates interested in computer vision, self-supervised learning, generative AI, and machine learning research.

Project 1: Self-Supervised and Meta-Learning for Rapid Adaptation
This project offers 2 PhD positions and 1 postdoctoral position. The research explores how pretraining data and learning objectives shape the priors learned by deep-learning models, and how those priors can be optimized for rapid adaptation to new tasks. The work combines novel dataset design, controlled benchmark environments, and methods for evaluating knowledge and problem-solving with the development of learning algorithms and architectures for efficient, robust, and transferable intelligence. This project is fully funded for four years and is carried out in close collaboration with Prof. Andrea Vedaldi at the University of Oxford.

Project 2: Collaborative World Models
This project offers 1 postdoctoral position working alongside two PhD researchers already recruited for the team. The research investigates collaborative world-model agents: visual models that predict environmental change, retain distinct perspectives and memories, and communicate to solve problems together. Topics include generative video modeling, self-supervised learning, memory, test-time adaptation, and decision-making, with applications in embodied AI and robotics.

Eligibility highlights: PhD applicants should hold, or expect by the start date, a master’s degree in computer science, engineering, mathematics, or a related field. Postdoctoral applicants should hold, or expect by the start date, a PhD in a related field and demonstrate a strong publication record in machine learning, deep learning, computer vision, or a closely related area. All candidates should have strong foundations in machine learning, deep learning, computer vision, applied mathematics, probability, and programming; experience with PyTorch or another major deep-learning framework is preferred; and English fluency is expected. Relevant experience in one or more of the listed topics is an advantage, but applicants are not expected to cover all areas.

Funding and benefits: All positions are fully funded within their respective projects. The university offers a competitive salary according to its regulations, with additional compensation for teaching duties. The group also provides access to the Swiss AI large-scale GPU cluster and support for conference, workshop, and training participation.

Application window: The positions start on October 1, 2026, or by agreement. Applications will be reviewed until excellent candidates are found; applicants are therefore encouraged to apply as early as possible.

How to apply: Submit your application through the CVG submission portal. Be sure to indicate whether you are applying for a PhD or postdoctoral position and name the project(s) you are interested in. Direct email applications will not be considered.

Institution: University of Bern, Bern, Switzerland.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

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