Through advances in artificial intelligence (AI), medical imaging has gained an increasingly important role in precision medicine. AI methods are being used both in radiology (“radiomics”) and pathology (“pathomics”) to develop prediction models that are at the basis of more precise and personalized clinical decision making. While radiomics and pathomics models often have similar goals and contain complementary information, these research fields are largely separated. Moreover, despite major advancements in these fields, implementation in real-world clinical practice remains limited.

To address this, the aim of the AI for Integrated Diagnostics (AIID) research line is to join forces of radiomics and pathomics to create trustworthy models to aid clinicians in decision making. Our mission is to develop multi-modal machine learning methods primarily for improved diagnosis and therapy response in oncology. While we focus on radiology and pathology, our vision is to extend our methods to additional datatypes. By smartly combining the different datatypes through AI, our methods could substantially impact clinical practice. The current project funded by a Dutch National Growth Fund AINed Fellowship kickstarts this research line with three PhD students and two Postdocs.

This PhD position focusses on developing novel deep learning methods, exploiting automated machine learning (AutoML) and meta-learning concepts. Instead of having to develop AI models from scratch considering only disease-specific datasets, you will develop a meta-level, pan-cancer method, learning simultaneously from various cancer types, both in radiomics and pathomics. Especially in rare cancers such as soft tissue tumors, this could have substantial impact. You will be the first to integrate AutoML and meta-learning, e.g., through Bayesian optimization, which will substantially impact the efficiency and thus feasibility of such methods.

You should be an independent and highly motivated researcher.

• You have a MSc or MEng degree in a (med)technical domain (e.g., Computer Science, Informatics, Physics, Engineering, Mathematics).
• Experience with automated machine learning, meta-learning, and/or programming is highly valued.
• You should be able to communicate and work with researchers from various fields. Excellent proficiency in English is required.

• You will receive a temporary position for 4 years.
• The gross monthly salary is € 2.901,- in the 1st year and increases to € 3.677,- in the 4th year (scale OIO).
• Excellent fringe benefits, such as a 13th month that is already paid out in November and an individual travel expense package.
• An International Office which aids you in preparing for you arrival and stay.
• Pension insurance with ABP, we take care of approximately 2/3 of the monthly contribution.
• Special benefits, such as an in-company physiotherapist and bicycle repairer. There is also a gym where you can work on your fitness after work.

Erasmus MC

The research will be conducted at the Biomedical Imaging Group Rotterdam (BIGR), which is part of the department of Radiology & Nuclear Medicine, and the PHANTOM group, which is part of the department of Pathology, both of the Erasmus MC, University Medical Center Rotterdam.
The department of Radiology & Nuclear Medicine is one of the largest imaging departments in Europe with 10 clinical MRI scanners at several field strengths. The department of Pathology conducts thorough analysis of diverse clinical, sequencing, and pathology imaging data, examining tissue samples from various tumors (e.g., tongue, breast, lung, liver, skin, eye, bladder, colorectal) using staining techniques like H&E, IHC, and IF. Both departments have an international and diverse character with a good balance between internationally recognised, high-end research and an excellent social working environment.

You will collaborate with a team of experienced researchers with backgrounds in image analysis, machine learning, radiology, pathology, surgery, and oncology. As part of the project you will also work with worldwide renowned experts in soft-tissue cancer within our Sarcoma Artificial Intelligence (SAI) consortium.

The supervision for the projects will be provided by associate professor Stefan Klein, PhD, and assistant professor Martijn Starmans, PhD.