Spectral data is used in various fields to assess detailed information on the microstructural content of materials or human tissue. Acquisition and interpretation of this data typically relies on highly trained and skilled experts, due to the complexity of the data and the high information content, only accessible through specialized data processing. This complexity and specialization limit the usage of these techniques by experts and hampers wider adoption. In healthcare, the availability of qualified MRI Radiologists is rapidly becoming a bottleneck: the number of patients grows at 8% per year, whereas workforce growth by only 3%, and can be expected to decline in aging societies. As a further complication, reading and reporting of complex Magnetic Resonance Imaging cases can take up to an hour. For spectroscopy, the time needed for data work-up and review can even be much longer, and requires trained and highly-skilled experts.

This research project will develop and leverage deep machine learning/artificial intelligence methods for simulation, denoising and enhancement of Magnetic Resonance Spectroscopy (MRS) dataset as well as methods for incorporating high-resolution structural imaging for improved spectrum analysis. The ultimate goal is to augment and replace expert users in data workup of MRS. The supervising team and embedding of the project bring together expertise on deep learning and the necessary deep physics knowledge base to ensure that developed AI solutions are robust against domain shift whilst delivering superior performance in each specific domain.

The project will be supervised by Prof. Dr. Marcel Breeuwer and Dr. Mitko Veta from the Medical Image Analysis Group (IMAG/e) at the TU/e Department of Biomedical Engineering. During the project you will closely collaborate with researchers from the TU/e Department of Electrical Engineering, Philips Healthcare and other industrial partners involved in the project. 

The successful candidate will become a member of the Medical Image Analysis Group (IMAG/e) at the Department of Biomedical Engineering, headed by Prof. Josien Pluim. The group consists of around 20 enthusiastic researchers, working on both methodological and applied innovations. Research topics include image analysis and quantification, and (deep) machine learning for oncology, cardiology, neurology and histopathology, as well as high-field MR imaging and RF safety. The group has strong ties with the Dutch industry and university medical centers.

• A master's degree (or an equivalent university degree) in Mathematics, Physics, Computer Science, Electrical Engineering, Biomedical Engineering or a related discipline
• Experience in (deep) machine learning/artificial intelligence
• Preferably experience in medical image analysis and/or medical image acquisition
• Excellent Python programming skills.

We are looking for candidates who have:

• The ability to contribute to cross-disciplinary collaborations
• A research-driven and proactive attitude
• Excellent written and oral communication skills in English.

TU/e aims to increase diversity among its employees and encourages applications from under-represented groups; in particular, female scientists are encouraged to apply.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.