PhD Student- Reinforcement Learning for Adaptive Radiation Therapy

Elekta Oncology Systems produces state of the art equipment and software for radiation oncology. Together they collaborate in the POP-AART lab (Partnership for Online Personalized AI-driven Adaptive RadioTherapy). The lab focusses on the use of AI to improve precision radiation in oncology. We are hiring a PhD students in reinforcement learning for this Lab. 

Radiotherapy treatment plans are designed by optimizing the parameters of the treatment delivery device such as beam directions, apertures, delivery times. Typically, such plans are designed based on a single CT scan of the patient. In practice, the anatomy of the patients changes continuously and adaptive radiation therapy aims to adjust the treatment plan accordingly. The goal of this project is to use reinforcement learning to optimize radiation treatment plans and adapt them in response to anatomical changes. 

POP-AART Lab 
You are going to carry out AI research (https://icai.ai/pop-aart-lab/ ) as part of the POP-AART lab at both the Netherlands Cancer Institute and the University of Amsterdam. There will also be regular interactions with researchers at Elekta Oncology Systems. In the lab, you will be supervised by prof. Sonke and dr. Gavves, as well as an assistant professor we are currently hiring. The lab will be part of the Innovation Center for Artificial Intelligence, a Netherlands initiative focused on joint technology development between academia, industry and government in the area of artificial intelligence.

Your tasks will be to:

• Develop new machine learning methods within the context of adaptive radiation therapy;
• Collaborate with other researchers within the lab, NKI, UvA and Elekta;
• Perform novel academic research on the crossroads of deep machine learning and medical physics. Research will be published in the top related, conferences, including ICLR, NeurIPS, ICML, CVPR, PAMI, MICCAI, AAPM, ICCR, PMB and Medical Physics.
• Complete and defend a PhD thesis within the official appointment duration of four years;
• Regularly present intermediate research results at international conferences and workshops, and publish them in proceedings and journals;
• Assist in relevant teaching activities.