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Professorship of Data-driven Materials Modeling Ph.D Position on Machine Learning in Biomechanics (m/w/d) 11.09., Wissenschaftliches Personal A Ph.D. position is available in the Professorship of Data-driven Materials Modeling to contribute to the development of efficient, certifiable, and practical solutions for inverse problems in diagnostic biomechanics. (For English see below) In unserer Professur ist eine Ph.D.-Position zu besetzen, um zur Entwicklung effizienter, überprüfbarer und praxisnaher Lösungen für inverse Probleme in der diagnostischen Biomechanik beizutragen, mit besonderem Schwerpunkt auf Elastographie. Das Projekt baut auf unserem kürzlich entwickelten Weak Neural Variational Inference (WNVI)-Framework auf und zielt darauf ab, dessen Fähigkeiten in folgenden Bereichen zu erweitern: Skalierbarkeit: Erweiterung der Methoden auf hochdimensionale und dreidimensionale Elastographie-Probleme unter Verwendung neuronaler Operatoren. Recheneffizienz: Entwicklung adaptiver und physikbewusster Strategien (z. B. optimierte Residuen-Auswahl, physikbasierte Zoom-Techniken) für Echtzeit-Inferenz. Praktische Anwendbarkeit: Aufbau robuster, benutzerfreundlicher Frameworks für multimodale Elastographie und Ermöglichung der Nutzung auf tragbaren Geräten (z. B. Smartphones) für Echtzeit-Diagnostik. Die Forschung vereint Kontinuumsmechanik, maschinelles Lernen, rechnergestützte Mathematik und probabilistische Modellierung mit direkten Anwendungen in der medizinischen Bildgebung und darüber hinaus. Details finden Sie unter dem folgenden Link ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ A Ph.D. position is available in our group to contribute to the development of efficient, certifiable, and practical solutions for inverse problems in diagnostic biomechanics, with a particular focus on elastography. The project builds on our recently developed Weak Neural Variational Inference (WNVI) framework and aims to advance its capabilities in the following directions: • Scalability: Extending methods to high-dimensional and three-dimensional elastography problems using neural operator representations. • Computational efficiency: Designing adaptive and physics-aware strategies (e.g., optimized residual selection, physics-based zooming) for real-time inference. • Practical usability: Developing robust, user-friendly frameworks for multimodal elastography and enabling deployment on portable devices (e.g., smartphones) for real-time diagnostics. The research combines continuum mechanics, machine learning, computational mathematics, and probabilistic modeling, with direct applications in medical imaging and beyond. Details can be found in the link below. The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.
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