PhD position on the Inverse Biophysical Modeling and Machine Learning in Personalized Oncology

Thursday 28th July 2022

Organization TUM
Location Munich, Germany
Title PhD position on the Inverse Biophysical Modeling and Machine Learning in Personalized Oncology
Email Address b.wiestler@tum.de, bjoern.menze@uzh.ch, ivan.ezhov@tum.de
Closing Date: 01.12.2022

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PhD position.
A PhD position on image-based personalization of radiotherapy planning for brain tumor patients is currently available at TUM, Munich. The position is supervised by Benedikt Wiestler (TUM) and Bjoern Menze (UZH). The salary is according to TV-L E13 (100%).

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Project 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).
Recently, we performed proof-of-concept studies with deep-learning based personalization techniques [1]. The goal of the PhD project is to pave the way between the existing or potentially newly developed personalization methodologies and clinical practice.

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Prerequisites.
Python (or C++), the ability to understand some math (probability theory, partial differential equations).

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Keywords.
Computational physiology, statistical inference, physics-informed deep learning

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For further information please feel free to reach out:
b.wiestler@tum.de,
bjoern.menze@uzh.ch,
ivan.ezhov@tum.de

References:
[1] Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling, https://arxiv.org/abs/2111.04090