PhD Position in Machine Learning and Computer Vision

Positions: We have an opening for a PhD position in the areas of machine learning and computer vision. The position is supported financially by a SNSF project, whose aim is to build con- trollable world models with deep learning methods. World models are generative models that predict future outcomes given past ones. We aim to work with both images and audio modalities and be able to use the world models to predict what would happen in the future if a certain condition of interest is met. This project is based on Unsupervised Learning, that is, it uses datasets that do not have human labeling/annotation (eg, text). Research will be performed in the Computer Vision Group at the University of Bern. The position can start from September 1, 2024, at the earliest and will be filled as soon as a suitable candidate is found.

Your profile: We are looking for a highly motivated candidate, who is eager to get involved in cutting edge, creative research. You hold a Master of Science in Computer Science, Mathematics or Engineering, with a solid background in machine learning and computer vision. You have excellent skills in applied mathematics, in probability theory, and a programming language (e.g., Python, C/C++). You have a solid background in Deep Learning and you are already a proficient programmer in one of the main Deep Learning libraries (e.g., TensorFlow, PyTorch). We expect fluent communication skills in English.

What we offer: You will be part of a team of academic researchers working on state-of-the-art technologies for machine learning and computer vision. You will have the chance to contribute to and participate in the international research community. We are located in Bern in the core of Switzerland, one of the cities with the highest quality of life worldwide. You will receive a very competitive salary (a base salary for the research + a separate teaching assistance salary) and be given financial support to attend training courses and international conferences.

Application and further information:

Applications must be submitted to Prof Paolo Favaro, through the submission website link http://cvg.unibe.ch/vacancies/

Applications submitted directly via email will not be considered.