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Diplomarbeiten, Bachelor- und Masterarbeiten
Sie suchen gerade eine Diplomarbeit, ein Thema für eine Bachelor oder Master Thesis? Dann sind Sie hier richtig. In diesem Bereich sind Abschlussarbeiten aus allen Fakultäten zu finden.
Beachten Sie auch den entsprechenden Stichwortindex.
Wenn Sie selbst eine Diplomarbeit ausschreiben wollen, lesen Sie bitte vorher unbedingt das 'Best Practice Manual Stellenanzeigen'.
01.06.2026
Bachelor's and Master's Theses or Research Internship at Professorship of Energy Management Technologies
You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like to work in a vibrant research environment? Then let’s design the energy systems of the future together!
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Kontakt: student-applications.emt@ed.tum.de
28.05.2026
[MA/SA] ML-Based Detection of User Intent and Rhetorical Signals in Symptom Checker Chatbots
This thesis investigates machine learning methods for detecting user intent and rhetorical signals in symptom checker chatbot messages. Using earlier chatbot study data and doctor–patient conversations, it compares rule-based, ML-based, and hybrid models for classifying messages as ethos, pathos, logos, or neutral, to support more adaptive and trustworthy healthcare chatbots.
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Kontakt: rutuja.joshi@tum.de
22.05.2026
Master’s Thesis - RSU-based Cooperative Perception using BEVFusion and ETSI ITS Communication
The Chair of Robotics, Artificial Intelligence, and Real-Time Systems offers a Master’s thesis focusing on extending an existing CARLA-based cooperative perception framework with Roadside Units (RSUs). The thesis investigates how infrastructure-based sensing can improve uncertainty-aware cooperative perception using BEVFusion and ETSI ITS communication standards.
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Kontakt: erik-leo.hass@tum.de
22.05.2026
Master’s theses: Creation of a Spatial Reasoning Benchmark
This thesis aims to develop a benchmark for evaluating the spatial reasoning of LLMs. Since AI models often struggle with 3D planning, LEGO bricks serve as a discretized medium to quantify this intelligence. Key tasks include generating synthetic datasets, defining metrics for spatial correctness, and validating AI-generated blueprints within 3D simulations.
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Kontakt: andre.schamschurko@tum.de
22.05.2026
[MA/BA/SA] Robust trajectory planning via Multi-Modal Sensor Fusion
This thesis aims to develop a multi-modal sensor fusion framework for robust trajectory planning in autonomous driving using multi-modal sensors in the simulation environment.
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Kontakt: chengdong.wu@tum.de
21.05.2026
[Master Thesis] Multimodal Contrastive Learning for MRI-Biomarker Discovery in Knee Osteoarthritis
This thesis aims to develop a self-supervised learning framework that combines contrastive pre-training on knee MRI with cross-modal knowledge distillation from paired clinical data. The goal is to discover candidate imaging biomarkers for knee OA and evaluate their representation quality, prognostic value and clinical interpretability.
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Kontakt: amna.gillani@tum.de, christina.valle@tum.de
13.05.2026
Master's Thesis in the "BayWater" Research Project
The team of Prof. Stephen Schrettl at the Technical University of Munich (TUM, Campus Weihenstephan) is offering a Master’s thesis opportunity within the collaborative research project BayWater, funded by the Bavarian Transformation and Research Foundation, in cooperation with Infineon Technologies AG at its site in Regensburg.
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Kontakt: application.fmp@ls.tum.de
12.05.2026
Master thesis or research internship: Sensor stabilization for optical ultrasound detection in non-invasive glucose monitoring
Development of a optical ultrasound detector for optoacoustic sensing towards non-invasive glucose monitoring.
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Kontakt: maximilian.gotsch@tum.de
11.05.2026
[Master Thesis with Bosch R&D] Reinforcement Learning for Behavior Planning in Automated Driving
Work on cutting-edge AI topics during your thesis and gain hands-on experience in an innovative environment. Ready to make an impact? Apply now and shape the future of automated driving!
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Kontakt: yuan_avs.gao@tum.de
11.05.2026
[Master Thesis with Bosch R&D] Bridging the Gap between Reinforcement Learning & End-to-End Driving
This thesis investigates the integration of Reinforcement Learning (RL) with end-to-end (E2E) autonomous driving approaches. While E2E methods rely on large amounts of expert data, RL enables learning through interaction in simulation. The goal is to explore how RL-based simulation and feedback mechanisms can improve the robustness and performance of state-of-the-art E2E driving policies.
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Kontakt: yuan_avs.gao@tum.de
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