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<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">PhD position.</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">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%).</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
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<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">Project description.</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">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).</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">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.</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
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<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">Prerequisites.</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">Python (or C++), the ability to understand some math (probability theory, partial differential equations).</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
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<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">Keywords.</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">Computational physiology, statistical inference, physics-informed deep learning</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
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<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">For further information please feel free to reach out:</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<a href="mailto:b.wiestler@tum.de" target="_blank" style="color: rgb(17, 85, 204); font-family: Arial, Helvetica, sans-serif; font-size: small;">b.wiestler@tum.de</a><span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">,</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<a href="mailto:bjoern.menze@uzh.ch" target="_blank" style="color: rgb(17, 85, 204); font-family: Arial, Helvetica, sans-serif; font-size: small;">bjoern.menze@uzh.ch</a><span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">,</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<a href="mailto:ivan.ezhov@tum.de" target="_blank" style="color: rgb(17, 85, 204); font-family: Arial, Helvetica, sans-serif; font-size: small;">ivan.ezhov@tum.de</a><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">References:</span><br style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">
<span style="color: rgb(34, 34, 34); font-family: Arial, Helvetica, sans-serif; font-size: small;">[1] Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling, </span><a href="https://arxiv.org/abs/2111.04090" target="_blank" data-saferedirecturl="https://www.google.com/url?q=https://arxiv.org/abs/2111.04090&source=gmail&ust=1659181674732000&usg=AOvVaw0EjWh1x1bIJMlU05d-Iq3n" style="color: rgb(17, 85, 204); font-family: Arial, Helvetica, sans-serif; font-size: small;">https://arxiv.org/abs/2111.<wbr>04090</a><br>
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