Connectionists: PhD position on the Inverse Biophysical Modeling and Machine Learning in Personalized Oncology, UZH/TUM

[Ezhov, Ivan]

We, at IBBM lab (UZH/TUM), seek PhD candidates to further develop computational approaches for solving the inverse problem in the context of brain tumor modeling with the aim to improve radiotherapy planning.



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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).



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


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



For contact details and further information please visit http://campar.in.tum.de/Students/phdDcomex

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