Open Student Thesis Offers

When writing your thesis at TUM I6, please follow our thesis guidelines. A guide to writing good thesis can be found here. A collection of useful material for research can be found here.

Systems and Software Engineering

Learning and Intuitive Robot Programming

Our group provides a range of topics related to human-robot interaction, intuitive robot programming, motion and grasp planning, deep learning, etc. 

Currently, the following thesis proposals are open:

KI.FABRIK: Future AI & Robotic Factory

Simulation-Based Learning Control for Real-World Robotic Manipulation and Navigation

For more information about other available topics in reinforcement learning, data-driven control and sim2real transfer, please contact Hossein Malmir.

DeepSLAM: Deep Learning based Localization and Mapping (Vision-based Perception and Navigation)

 

Autonomous Vehicles

The Cyber Physical Systems group is pursuing a wide range of research directions related to safe decision making, motion planning and control for autonomous vehicles, involving both formal methods, sampling- and optimization-based methods as well as deep learning-based methods.

Currently, the following thesis proposals are open:

Fleet Management, AV and EV simulation, Federated Learning Simulations

Reach out to me if you are interested in any of the following topics -

  • Fleet management (Autonomous Mobility on Demand)
  • Simulation of Federated Learning

Email - nagacharan.tangirala@tum.de

 

Spiking Neural Networks - Next Generation AI for Autonomous Driving

At the KI-ASIC project we are researching about the application of bio-inspired neural networks to real-world applications.

If you are interested in learning about neuroscience and how neuromorphic engineering is trying to narrow the gap between biology an technology, do not hesitate to contact us.

Available:

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Previous topics:

Safe Reinforcement Learning in Robotics

Currently open positions:

Additionally, we will have open topics in safe reinforcement learning for manipulators and mobile platforms in the future.
If you are interested in these topics, you can contact: jakob (dot) thumm (at) tum.de

Modular Robotics

For interest in a BA/MA thesis in machine learning for modular robotics, please contact Jonathan Kuelz or Matthias Mayer.

Currently open:

Deep Learning for Computer Vision

If you are interested in researching trending topics in the field of computer vision and pattern recognition:

  • Object detection
  • Segmentation
  • Tracking
  • Surface reconstruction
  • Mesh generation
  • Text-to-image generation
  • Domain adaptation of synthetic data

then please contact: Bare Luka Žagar

Safe Massively Multi-Agent Reinforcement Learning

We have several topics about Reinforcement Learning, Robotics,  Autonomous Driving and AI Safety, for more information, please contact Shangding Gu.

Safe Reinforcement Learning, Multi-Agent Reinforcement Learning

[BA/MA]: Provably Safe Reinforcement Learning Control of a Quadrotor

Machine Learning Algorithms for Hybrid Vehicle Data

Please see this page for the available topics about 3D Object Detection and Tracking.

Project SunRISE: Anomaly Detection in Time-Sensitive Networks

Autonomous Robot & Visual Servo & Deep Learning & Robot Design & Medical Robotics

For more information, please visit my homepage Mingchuan Zhou or contact me via email (zhoum@in.tum.de).

Data Analysis and Deep Learning in Robot-Based Additive Manufacturing

BA / IDP / MA - partly in cooperation with Siemens AG

For more information, please visit my homepage Raven Reisch or contact me via email (raven.reisch@tum.de).

OSBORNE (Future Automotive E/E Architectures for Autonomous Cars)

We have a set of open topics in the domain of affective computing and multimodal emotion recognition, within the context of OSBORNE project, for more information please contact Sina.

HORSE / REMODEL

We have several open topics related to computer vision in the HORSE and REMODEL projects. Please contact Arne for more details.

Collaboration with Chair for Product Development and Lightweight Design