We have openings for 4 PhD positions in the areas of machine learning and computer vision. Two positions focus on cutting edge research in machine learning for medical data (in particular temporal signals such as EEG/iEEG) and the other two positions for image and video analysis. Research topics include the development and analysis of generative models for temporal data, of feedback models that incorporate control signals, and the development of unsupervised/weakly supervised/reinforcement learning methods. Research will be performed in the Computer Vision Group at the University of Bern. Two positions are available from now and two will be available on January 2019 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 computer vision and/or machine learning. 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 and be given financial support to attend training courses and international conferences.
Applications must be submitted to Prof Paolo Favaro here with PHD18 in the subject line.
Applications must include in a single pdf file (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 please add a link in the submitted pdf to a cloud directory and make it accessible.
You can download the problem set here.