Function description
Cancer is the second leading cause of death worldwide. Radiation therapy, or radiotherapy (RT) for short, plays a pivotal role in treating many cancers, where approximately 50% of cancer patients can benefit from RT in the management of their disease. During radiotherapy, ionizing radiation, generally produced by a linear accelerator (linac), is delivered with the intent of killing malignant cells. Treatment with radiotherapy is typically fractionated and delivered (in the so-called fractions) for several days or weeks to limit side effects and exploit the higher repair capacity of normal cells versus tumor cells. We aim to improve the quality, efficiency, and safety of radiotherapy by developing novel adaptive radiotherapy (ART) techniques using magnetic resonance imaging in MR-guided radiotherapy (MRgRT).
Using MRgRT, where an MRI scanner is integrated with the linac, we can acquire MR images prior and during treatment. In contrast to any other radiotherapy treatment paradigm, this combination gives the possibility to adapt real-time to current (internal) motion in the patient. This project will develop novel AI techniques enabling real-time tracking of the tumor and sensitive organs (the organs-at-risk). To do this, we will develop AI-based accelerated MRI techniques, combined end-to-end with real-time tracking and registration algorithms. To accelerate the acquisition even further, this will be combined with active acquisition MRI techniques where reinforcement learning agents determine the next sample in k-space, leading to a lower uncertainty on the current target position. The developed techniques' clinical feasibility will subsequently be evaluated and validated in cancers in the upper abdominal region, where motion is an essential difficulty for radiotherapy.

The job description
As a PhD-candidate, you will be responsible for developing state-of-the-art deep learning techniques for active acquisition, reconstruction, and tracking of raw MRI data. Using unrolled primal-proximal reconstruction schemes and reinforcement learning techniques, you will develop active acquisition techniques for real-time MRI. You will develop deep learning-based registration and tracking algorithms to enable real-time tracking in this accelerated MRI data. Finally, you will validate these algorithms in independent cases to ensure the devised AI-algorithms' usefulness in clinical practice.
You are embedded in the ICAI AI for Oncology Lab, a collaboration between the Netherlands Cancer Institute and the Informatics Institute of the University of Amsterdam. The lab's mission is to develop innovations in artificial intelligence for the improvement of diagnosis and therapy of cancer. You will discuss results with our team, publish your work in artificial intelligence / medical journals, and present it at international conferences. The daily supervisor in this project is Dr. J. Teuwen, lab manager of the ICAI AI for Oncology Lab. Other team members are the scientific directors Prof. Dr. C. Sanchez (UvA) and Prof. Dr. J-J. Sonke.

We are looking for an enthusiastic PhD student (or postdoc) who will develop novel deep learning algorithms and shape the research in collaboration with the project leaders, AI, and medical experts. 
We are looking for a motivated, goal-oriented, independent, and proactive PhD candidate or postdoc who is enthusiastic about working in a multidisciplinary setting. Preferably you have a master's degree in artificial intelligence, computer science, physics, mathematics, medicine, or equivalent. In any case, you should have experience with deep learning and have excellent programming skills. Your experience should be evident from the courses followed and your GitHub account.

Fixed-term contract: 3 or 4 years.

You will have the opportunity to follow high-quality courses offered by the OOA oncology graduate school throughout your PhD. The employment will be for a period of 4 years. The gross salary per month for our PhD will range from € 2.884,- to a maximum of € 3.322,- according to the standard PhD scales. The terms of employment will be in accordance with the CAO Ziekenhuizen (Collective Labor Agreement for Hospitals). 
Regarding the postdoc, the employment will be for a period of 3 years. The gross salary will range from € 3.447,- and € 4.077,- per month for a fulltime position, according to the FWG-function group 55 (postdoc), depending on previous experience. The terms of employment will be in accordance with the CAO Ziekenhuizen (Collective Labour Agreement for Hospitals). 
In addition you will receive a fixed end-of-year bonus in December (8,33%) and in May you will receive 8,33% holiday pay. For more information in regard to the secondary conditions please contact the recruiter via: c.de.santis@nki.nl.

Netherlands Cancer Institute

The Netherlands Cancer Institute comprises an internationally acclaimed research institute as well as a dedicated cancer clinic. This combination ensures rapid translation of basic research into clinical applications: today’s research for tomorrow’s cure.