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Studentische Hilfskräfte, Praktikantenstellen, Studienarbeiten
03.05.2024
Master Thesis: Probabilistic machine learning for natural hazard loss predictions
Losses caused by natural hazards are constantly increasing, but accurate models for these losses are still lacking, mostly due to lack of appropriate data.
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Kontakt: Amelie Hoffmann (amelie.hoffmann@tum.de)
02.05.2024
Student Research Assistant(s) for Project on AI Ethics Literacy in Educational Technology w/ Focus on Web App Development
Kontakt: matthias.grabmair@tum.de
02.05.2024
Development of Computer Vision module for environment perception and motion planning (Hiwi, Research Internship)
Kontakt: Dipl.-Ing. Ioannis Xygonakis (ioannis.xygonakis@tum.de) Riddhiman Laha (riddhiman.laha@tum.de)
02.05.2024
BCI-Gym: Development of Virtual Environments for Brain-Computer Interface users training (Hiwi, Research Internship)
Kontakt: Dr. Alexander Craik (alex.craik@tum.de) Dipl.-Ing. Ioannis Xygonakis (ioannis.xygonakis@tum.de)
29.04.2024
Kommunikative Studentische Hilfskräfte (m/w/d)
Die Studieninformation der Technischen Universität München sucht zum 01.06.2024 kommunikative studentische Hilfskräfte zur Unterstützung. Wir sind hochschulweit die erste Anlaufstelle für Studieninteressierte, Studienbewerber:innen und für Studierende aus dem In- und Ausland. Wichtig für diese Stelle ist eine gute Verfügbarkeit in den Monaten Juli bis Oktober. Langfristige Mitarbeit über zwei Semester(12 Monate) ist wünschenswert.
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Kontakt: hotline-bewerbung@tum.de
26.04.2024
Studentische Mitarbeitenden-Position (10-20 h / Woche) am Department of Educational Sciences für das Projekt CLiViE
Die Forschungsgruppe Learning Sciences and Educational Design Technology (Prof. Dr. Anna Keune) am Department of Educational Sciences der Technischen Universität München (TUM) schreibt eine Stelle im Forschungsprojekt “CLiViE” aus.
Beabsichtigter Arbeitsbeginn ist so bald wie möglich.
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Kontakt: anna.keune@tum.de
26.04.2024
Master Thesis: Deep Learning for Brain Tumor Modeling
We model brain tumor growth for improved radiotherapy planning based on 3D magnetic resonance images (MRI). Partial differential equation (PDE) based models have the potential to personalize glioma therapy. However, calibrating these models to individual patients is computationally expensive using traditional numerical solvers and classical optimization techniques.
First tests with simple tumor models show promising results using differentiable deep learning based solvers for fast, gradient based optimization. Thereby a neural network inputs the specific tumor parameters like growth and diffusion rate and predicts a tumor cell concentration inside the brain [1]. To calibrate this model for a specific patient, the input parameters are optimized utilizing the differentiability of the network with respect to the input.
Your role would be to utilize state-of-the-art network architectures to develop a deep learning-based solver for more realistic brain tumor growth models.
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Kontakt: j.weidner@tum.de
26.04.2024
Bachelor / Master Thesis: What was before the brain tumor? Recovering the original state of the brain for digital brain tumor twin.
Recovering the original state of the brain for digital brain tumor twins using MRI.
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Kontakt: Jonas Weidner j.weidner@tum.de
26.04.2024
Praktikant*innen gesucht!
Die Professur Psychology & Digital Mental Health Care vergibt Forschungspraktika im Forschungsbereich eMental Health.
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Kontakt: simona.maspero@tum.de
24.04.2024
Studentische Hilfskraft für die Professur für Wissenschafs- und Technologiepolitik gesucht
Kontakt: applications.stp@mcts.tum.de
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