Postdoctoral Position in medical physics (M/F)

Laboratory

The Laboratory of Translational Imaging in Oncology (LITO) is a research unit (U1288) supported by Inserm (= French NIH) and Institut Curie, the first cancer center in France (https://www.lito-web.fr/). LITO includes approximately 30 researchers, including physicists, engineers, physicians, pharmacists, and technologists. The Orsay Proton therapy Center (CPO), founded in 1991, is part of the radiation oncology department at Institut Curie hospital group, which is one of the European-wide recognition as a Comprehensive Cancer Center of excellence.

Position

Radiotherapy is currently one of the main techniques used for the treatment of cancer. During the last thirty years, numerous technical advances have allowed to considerably improve the conformation of the irradiations to the specific characteristics of each tumour and to reduce their side effects. Nevertheless, the tolerance of healthy tissues remains the main limitation of this type of treatment, especially in the case of particularly radiosensitive patients, such as children, or radioresistant tumours for which the control of the side effects of radiotherapy remains a major therapeutic challenge.

The development of innovative approaches that reduce the sensitivity of healthy tissues to irradiation while maintaining the efficacy of the treatment on the tumour is therefore of crucial importance for the progress of the efficacy of radiotherapy. Recently, pioneering work at the Institut Curie demonstrated that ultra-high dose rate irradiation or spatially fractionated radiotherapy could have a major healthy tissue sparing effect while preserving anti-tumour efficacy.

Within the radiation oncology department and LITO team based at the Institut Curie- Hospital Orsay (91), the medical physics' team is recruiting a postdoctoral fellow, with a strong interest in translational research on cancer treatment. As part of the activities of this project, the applicant would be expected to leverage machine and deep learning for predicting patient response to treatment as well as radiation therapy toxicity, especially in the context of innovative radiotherapy techniques (FLASH, protons, VHEE).

The tasks will include:

• Using radiomics and machine/deep learning to predict patient response to cancer treatment, with a focus on incorporating uncertainty calculations and model explainability/interpretability methods.

• Analyzing large, high-quality datasets using innovative image-registration, machine learning, and dose characteristics (dose-based radiomics), to develop mathematical models that could be used to predict and avoid key morbidity/toxicity endpoints in radiotherapy treatment planning.

• Conduct thorough evaluations and validations of the developed algorithms using clinical datasets.

• Contribute to the documentation and dissemination of research findings through reports and presentations.