At the Department of Signals and Systems, we conduct internationally renowned research in biomedical engineering, antenna systems, signal processing, image analysis, automatic control, automation, mechatronics, and communication systems. We offer a dynamic and international research environment with about 150 employees from more than 20 countries, and with extensive national and international research collaborations with academia, industry and society. The department provides more than 70 courses, of which most are included in the Master’s Programs ”Biomedical Engineering”, ”Systems, Control and Mechatronics”, and ”Communication Engineering”.

Information about the research group and the project
The Computer Vision Group conducts research in the field of automatic image interpretation. The group targets both medical applications, such as the development of new and more effective methods and systems for analysis, support and diagnostics, as well as general computer vision applications including autonomously guided vehicles, image-based localization, stereo and structure-from-motion. The main research problems include mathematical theory, algorithms and machine learning for inverse problems such as reconstruction, segmentation and registration.

Locating and segmenting anatomical structures such as the heart, vertebrae or different regions of the brain in a three-dimensional image is an important step for many clinical applications, including visualization, surgical planning and dose radiation therapy. In this project, the target is to develop new robust optimization techniques for image registration and segmentation that can be used for real-time image-guided surgery.

The research will be focused on mathematical tools appropriate for the above challenge in close collaboration with medical partners. Two relevant examples from our current research are tractable optimization methods for Markov Random Fields (MRFs) and robust model estimation. MRFs in combination with machine learning have proven to be a very useful tool for image registration and segmentation. However, for complex MRF models the inference problem is very hard. We address this problem by building a framework based on submodular relaxations. By using guaranteed approximation algorithms we hope to achieve a golden mean between the quality of the computed solution and tractability. MRFs are high-dimensional models that are good at capturing local appearance. At the other end of the scale we find low-dimensional geometric models that are well suited to model global biological shape variations and transformations. In order to successfully apply such models, it is necessary to cope with abnormalities and outliers in the data. A goal for this project is to develop exact algorithms for robust model estimation but also approximation algorithms suitable for higher dimensions and large-scale problems.

Major responsibilities
Your major responsibilities are to pursue your own doctoral studies. You are expected to develop your own scientific concepts and communicate the results of your research verbally and in writing, both in Swedish and in English. The position generally also includes teaching on Chalmers' undergraduate level or performing other duties corresponding to 20 per cent of working hours.

Position summary
Full-time temporary employment. The position is limited to a maximum of five years.

Qualifications
To qualify as a PhD student, you must have a master's level degree corresponding to at least 240 higher education credits in a relevant field (physics, mathematics or computer science).

The position requires sound verbal and written communication skills in Swedish and English. If Swedish is not your native language, you should be able to teach in Swedish after two years. Chalmers offers Swedish courses.

For further information and how to apply, please visit: 
http://www.chalmers.se/en/about-chalmers/vacancies/?rmpage=job&rmjob=2960…