PhD position on the inverse problem of brain tumor modeling (UZH/TUM)
Sunday 20th December 2020
Closing date. 30.04.2020
Description. Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it can be viewed as an evaluation of a system of partial differential equations, wherein the underlying physiological processes that govern the growth of the tumor, such as diffusion and proliferation of tumor cells, are considered. To personalize the model, i.e. find a relevant set of parameters, with respect to the tumor dynamics of a particular patient, the model can be informed from empirical data, e.g., medical images obtained from different diagnostic modalities, such as magnetic-resonance imaging (MRI) or positron-emission tomography (PET).
We seek PhD candidates to further develop computational approaches for solving the inverse problem in the context of brain tumor modeling. The methods will be validated under models of various biophysical complexity on several medical imaging datasets.
Keywords. Computational physiology, statistical inference, physics-informed deep learning
Prerequisites. Python/C++, ability to understand some math (probability theory, partial differential equations).
1. Menze, Bjoern H., et al. "A generative approach for image-based modeling of tumor growth." IPMI 2011.
2. Lipkova, Jana, et al. "Personalized radiotherapy Design for Glioblastoma: integrating mathematical tumor models, multimodal scans, and Bayesian inference." IEEE TMI 38.8 (2019): 1875-1884.
3. Ezhov, Ivan, et al. "Neural parameters estimation for brain tumor growth modeling." MICCAI 2019.
If interested, please send us your updated CV and transcripts.
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