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Research Fellow (f/m/d) in Machine Learning for Radiation Therapy
Published(86 days ago)
Deutsches Krebsforschungszentrum / German Cancer Research Center (DKFZ)Heidelberg
The German Cancer Research Center is the largest biomedical research institution in Germany. With more than 3,000 employees, we operate an extensive scientific program in the field of cancer research.
The Division of Medical Physics in Radiation Oncology is seeking a
Research Fellow in Machine Learning for Radiation Therapy
The new BMBF funded ARTEMIS project is focused on the establishment of a unique MR-guided ion-beam therapy prototype for clinical application. The continuous monitoring with high soft-tissue-contrast allows to complement the precision of ion beam irradiation with an accurate organ and target volume tracking. As up till now ion treatment planning relies on the planning CT to stopping power conversion, substitution of the CT by the MRI modality poses the challenge to predict the stopping power distribution of each patient from an MRI.
We invite applications for an enthusiastic research fellow to join the collaboration between DKFZ and the University Hospital Heidelberg to develop a correlation model for pseudo-CT generation in a MR-only planning scenario utilizing cutting-edge machine learning methodologies.
In ARTEMIS, you will work within a research group at DKFZ collaborating with researchers and clinicians in the field of radiation oncology. You will exploit the unique data source generated in Heidelberg, a hub for MR-guided treatments, to establish a prediction model for creation of digital patient twins to optimize radiation treatment planning.
Analyze anonymized MR-CT datasets of patients and phantom measurements
Design and establish machine learning models and pipelines for MR-only pseudo-CT generation process (or directly stopping power maps)
Scrutinize the model robustness and accuracy in comparison to image registration based pseudo-CT generation methodology
Present your findings in international conferences and in scientific publications
Design and implement a deployment channel for model utilization to the clinical partner site
PhD (or master‘s degree plus 3 years relevant experience) in computer science, computational physics, medical informatics or equivalent
Demonstrated experience in machine learning on medical images
Previous experience with medical imaging data like CT and MRI and radiation treatment concepts is desired
Working experience with deep learning models (e.g. UNET) utilizing GPU-accelerated Tensorflow is desired
Solid programming skills in Python (C++ and/or CUDA is a plus)
Excellent interdisciplinary communication, presentation, and scientific writing skills in English
You are a highly motivated data scientist with the desire to contribute to the improvement of ion-beam therapy planning. You are passionate about getting clinically relevant knowledge out of real-world datasets and are looking forward to collaborative interdisciplinary work. You have an independent and pro-active work style but also interact well in a team environment. The position will give you the opportunity to work on a unique clinical prototype for MR-guided ion-beam therapy. You will have opportunities to supervise and mentor junior researchers during the appointment. The option exists to earn a doctoral degree if desired.
The position is limited to 2 years with the possibility of prolongation.
The position can in principle be part-time.
For further information please contact
Dr. Kristina Giske, phone +49 6221 42-2579.
The German Cancer Research Center is committed to increase the percentage of female scientists and encourages female applicants to apply. Among candidates of equal aptitude and qualifications, a person with disabilities will be given preference.
To apply for a position please use our online application portal
We ask for your understanding that we cannot return application documents that are sent to us by post (Deutsches Krebsforschungszentrum, Personalabteilung, Im Neuenheimer Feld 280, 69120 Heidelberg) and that we do not accept applications submitted via email. We apologize for any inconvenience this may cause.
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