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PhD on machine learning for explainable neurovascular image analysis

Updated: 08 Feb 2025

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Research / Academic
Eindhoven

Are you passionate about trustworthy AI and brain image analysis and want to make an impact in transforming next-generation clinical care? We are offering a fully funded, 4-year PhD position for a motivated young scientist to develop and validate novel AI-based image analysis techniques in the diagnosis of neurovascular diseases.

Job Description

Recent years have witnessed significant advancements in deep learning and its widespread applications in medical image analysis. However, the integration of these AI-based tools into clinical workflows remains limited. One major obstacle limiting clinical adoption is the lack of explainability in current models. Clinicians are often reluctant to rely on algorithms with opaque decision-making processes. This situation underscores the pressing need for advanced machine learning techniques that not only deliver high performance but also provide interpretable insights into how conclusions are drawn from medical images. Addressing this need is essential for building clinical trust, facilitating the acceptance of AI-driven decision-support systems, and ultimately improving the efficiency and quality of patient care.

This PhD project aims to foster clinical trust by advancing explainable AI for image-based diagnosis and treatment decision-support in neurovascular diseases, with a focus on stroke and intracranial aneurysms. You will develop and validate innovative methodologies that improve informed decision-making in medical image analysis, including detecting subtle changes, highlighting abnormalities in medical images, and quantifying feature contributions and associated uncertainties.

You will be embedded in the Medical Image Analysis Group, which is part of the cluster Biomedical Imaging & Modeling within the Department of Biomedical Engineering. The group includes six assistant/associate/full professors and in total consists of around 20 enthusiastic researchers, working on both methodological and applied innovations. Topics in the research lines of our faculty members include image analysis, quantification and machine/deep learning for oncology, cardiology, neurology and histopathology, as well as high-field MR imaging and RF safety. The group has strong ties with the University Medical Center Utrecht (both in research and education) and with Philips NL, but also collaborates with other clinical institutes and industry.

As part of the IMAG/e group, you will contribute to the understanding and development of explainable deep learning techniques and foster clinical adoption of AI in modern medical care. This project also offers a collaborative and multidisciplinary environment, with opportunities to work alongside leading national and international partners in the field.

Requirements:

  • A master's degree (or an equivalent university degree) in Biomedical Engineering, Computer Science, Electrical Engineering, or related disciplines with excellent grades.
  • A background in AI and medical image analysis is highly preferred.
  • A research-oriented attitude with good analytical skills.
  • A team player that enjoys working in multicultural and interdisciplinary teams.
  • Good communication and organization skills.
  • Fluent in spoken and written English (C1 level).

Salary Benefits:

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

  • Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
  • Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,901 max. €3,707).
  • A year-end bonus of 8.3% and annual vacation pay of 8%.
  • High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
  • An excellent technical infrastructure, on-campus children's day care and sports facilities.
  • An allowance for commuting, working from home and internet costs.
  • A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 
Work Hours:

38 hours per week

Address:

De Rondom 70