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PhD for investigating Robust and reliable Artificial Intelligence for oncology

PhD for investigating Robust and reliable Artificial Intelligence for oncology

Electrical Engineering



Early detection, accurate diagnosis/staging of cancer, and subsequent selection of the appropriate treatment are critical factors to improve patient outcomes. Diagnostic imaging plays an integral role in many phases of a patient’s care path, but considerable challenges remain regarding the accuracy, efficacy and efficiency. Artificial Intelligence (AI) holds great promise to support image analysis to detect, characterize, and monitoring disease. AI can enable automated segmentation and support in diagnosis (e.g. classifying abnormalities as benign or malignant) and staging (categorizing tumors into pre-defined groups based on expected disease course and treatment strategy). Furthermore, it can support cancer monitoring by capturing image features over time to evaluate the patient’s response to treatment.

Many AI solutions for radiology have been developed in research labs, but few have been adopted successfully in clinical practice, limiting their impact on patient outcomes. A well-recognized cause for this poor adoption is a lack of human-centered design of AI, leading to solutions to clinically insignificant problems, too much or too little trust in AI outcomes and poor fit of AI solutions in clinician’s ways of working. This project will therefore develop tools, methods and assets to bring AI to clinical practice and evaluate actual use, trust, and experience with AI. The design of optimal physician-AI collaboration combines data science and UI/UX expertise to address technical challenges, such as AI explainability, robustness and uncertainty.

Advancing Cancer care through Interpretable AI

The Eindhoven MedTech Innovation Center (e/MTIC) is a public-private partnership aimed at creating and growing an ecosystem that offers a fast track to high-tech health innovations. Within this ecosystem, we aim to create impactful innovations in the oncology domain, through close multidisciplinary collaboration between an academic partner (Eindhoven University of Technology), a top-clinical hospital (Catharina Hospital Eindhoven) and an industrial partner (Philips). In this triangle, the research project “Advancing Cancer care through Interpretable AI” (ACACIA) aims to advance clinical decision support for cancer care with Artificial Intelligence (AI), taking a patient-centered and physician-centered approach to optimize physician-AI collaboration and adoption of AI in clinical practice. This project focusses on the integration of AI in CT-based imaging systems and builds on prior work that has been developed within e/MTIC in recent years.

You will be part of an innovative multi-disciplinary team

This PhD position is embedded in a multidisciplinary team of in total 4 PhD positions covering machine learning, product design and clinical expertise. The position advertised here focusses on ‘Bringing AI into clinical practice’, including clinical data collection, user testing and clinical evaluation of the AI solutions.. Around the core team, a group of domain experts is closely involved in the project, supporting the PhD researchers. This group consists of medical specialists such as radiologists and surgeons, but also experienced research scientists from Philips and Eindhoven University of Technology. The project activities are organized in 3 tightly interlinked workpackages:

WP1. Machine Learning for robust & trustworthy AI

This activity, led by a machine learning PhD student, aims to develop robust and trustworthy
AI in medical imaging. It will address a subset of clinical application areas (e.g., lung cancer and pancreas cancer), by development and maturation of AI methods for the selected clinical use cases. Research topics include, but are not limited to, AI transparency, interpretability and explainability, self-critical AI, confidence quantification and out-of-distribution detection.
A more detailed description is provided below.

WP2. User experience and interface design and evaluation for optimal physician-AI collaboration

This activity, led by a design PhD student, aims to iteratively develop and evaluate UI/UX innovations to shape optimal physician-AI collaboration, as well as methods to evaluate the clinical experience with end-users. This will drive the AI models developed in WP1 and integrate AI-based outputs generated in WP1.

WP3: Clinical data collection, user testing and evaluation

In this activity, led by two clinical PhD students, the AI algorithms developed in WP1 and clinician-AI collaboration innovations developed in WP2 will be evaluated in clinical practice. These comprise the evaluation of AI-based lung nodule assessment, evaluation of AI-based pancreatic cancer detection and resectability assessment and testing scalability of the innovations to a third cancer type (e.g. other abdominal cancer such as kidney cancer). We are interested in what is needed for an optimal physician-AI collaboration and what are benefits of AI solutions in the field; e.g. adoption and appropriate use, impact on patient outcomes, physician experience while working with the AI solution.

The 4 PhD candidates will closely collaborate and share data, AI models, technological platforms and methodologies as complementary focus points of each PhD position.

The clinical PhD position advertised here focuses on the clinical aspects of this project. Specific tasks goal include:

  • Collecting clinical data and scans;
  • Annotating CT-scans;
  • Providing clinical requirements for the AI models and their usage in clinical workflows
  • Checking and correcting output of the developed algorithms;
  • Measuring benefits of AI-clinician collaboration
  • Correlating AI results with follow-up and outcomes


The envisioned candidate for the open position holds an MSc degree in Medicine or Technical Medicine. Interested MSc’s in Biomedical Engineering are also invited to respond.

The candidate should have:

  • Strong communication skills, including an excellent proficiency in English (spoken and written);
  • An affinity with Radiology and interest in Artificial Intelligence;
  • Eagerness to spend part of his/her time on-site with each partner (TU/e, Philips, Catharina Hospital) so as to fully apprehend and profit from the expertise, capabilities and facilities of each partner (all partners are located in close proximity to each other).


  • Envisioned starting date of PhD project preferably early 2022.
  • A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
  • full-time employment for two years, with an intermediate evaluation after one year and a possible extension beyond year two.
  • To support you during your PhD and to prepare you for the rest of your career, you will have free access to a personal development program for PhD students (PROOF program).
  • A salary in accordance with the Collective Labor Agreement of the Dutch university (with a gross monthly salary of € 2.443,00 in the first year).
  • Benefits 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.
  • 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

Informatie en sollicitatie

More information

Do you recognize yourself in this profile and would you like to know more? Please contact
Joost Nederend, Joost.Nederend[at]

For information about terms of employment, click here or contact HRServices.flux[at]

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We invite you to submit a complete application by using the 'apply now'-button on this page.
The application should include a:

  • Cover letter in which you describe your motivation and qualifications for the position.
  • Curriculum vitae, including a list of your publications and the contact information of three references.
  • Brief description of your MSc thesis.

We look forward to your application and will screen it as soon as we have received it.

We do not respond to applications that are sent to us in a different way.

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