Deadline: This submission webpage will be closed as soon as all the positions are filled.
We have openings for 4 PhD positions in the areas of machine learning and computer vision. Each position covers one of these topics: Self-Supervised Learning, Disentangling Factors of Variations, Unsupervised Learning, and Learning Interactions. Research objectives include the development and analysis of generative models for images and videos, and the development of novel machine learning methods. Research will be performed in the Computer Vision Group at the University of Bern. All 4 positions are available from July 2020 and will be filled as soon as a suitable candidate is found.
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, Caffe). We expect fluent communication skills in English.
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 of 55,000 CHF per year + a teaching assistance salary) and be given financial support to attend training courses and international conferences.
Applications must include the following documents (file size limit of 3MB): CV, BSc and MSc degree certificates and transcripts, two reference letters and answers to the questionnaire. For additional files such as theses and publications or other references submit a link to a cloud directory and make it accessible.