Open Positions
We are looking for exceptional candidates who are interested in working on projects at the interface of robotics, controls and machine learning. People in our group come from different backgrounds including mechanical engineering, electrical engineering, engineering science, computer science, and physics. For an overview of the projects that we are working on, please visit our research page and have a look at some of our publications. Projects can range from strongly theory/mathematics-focused to being an even mix of theory and experimental. This video gives you a glimpse into our research.
In addition to the positions mentioned, we are also hiring through interdisciplinary programs such as MCML and relAI; please check the websites for opportunities and application procedures.
Who We Are
The Learning Systems and Robotics Lab at the Technical University of Munich is led by Prof. Angela Schoellig. Our research is motivated by the vision of a seamless interaction of robotic systems with the physical world. In particular, our research interests are centred around the challenges associated with robots operating in increasingly unstructured, uncertain and changing environments, and over long periods of time. These situations challenge current robot designs, which rely on knowing the specifics of the environment and tasks ahead of time in order to operate safely and efficiently. We address this problem by drawing ideas from controls, machine learning and optimization. We believe that the next generation of robot algorithms will combine a-priori information about the robot and its environment with data collected during operation.
Our research projects fall into two categories:
- developing novel robot control and learning algorithms that enable single and multi-robot systems to operate safely and effectively in real-world application scenarios, and
- working in interdisciplinary teams on robot applications to various challenging problems.
Visit our research page (https://www.learnsyslab.org) to learn more about our latest work as well as academic and industry collaborations.
Benefits of Working with Us
- Leading lab in Safe Robot Decision-Making: Our lab has developed several advanced frameworks including Gaussian Process Model Predictive Control (GP-MPC) for safe learning from data, multi-robot transfer learning, and Bayesian-optimization-based safe learning, as well as distributed MPC (DMPC) and alternative minimization (AM) algorithms for swarm coordination.
- International Research Partnerships: We collaborate with top research institutions and labs worldwide. Our partners span across top-ranked universities such as MIT, ETH Zurich, the University of Toronto, and the TUM. We are also part of various networks, including the Robotic Institute Germany (where we take a leading role), the Konrad Zuse School of Excellence in reliable AI (relAI), the Munich Center for Machine Learning (MCML), and the Vector Institute for Artificial Intelligence.
- Industry Collaborations and Large-Scale Demonstrations: Our industry collaborations span across multiple sectors and extend to leading institutions across Europe, North America, and beyond. As a part of the collaborations, we have completed various large-scale demonstrations such as aerial vehicle testing in Africa, the USA, and Mexico, and deploying self-driving platforms in Arizona, Michigan, and the Canadian Space Agency’s Mars Emulation Terrain.
- International Recognitions: The research work from the lab was recognized by several international research awards (e.g., best paper and presentation awards at robotics conferences, first prize in the MIT Enabling Society Tech Competition, finalist of the $1M Drones for Good Competition, and the winner of the North-American SAE AutoDrive Challenge six times).
- Extensive Research Facilities: We are well equipped to support advanced robotics research (e.g., a robot hall with a motion capture system, platforms such as aerial swarms, mobile manipulators, robot arms, and ground vehicles, compute resources for simulations, data processing, and machine learning).
- Visiting Researchers and Exchange Programs: We regularly host visiting researchers and engage in exchange programs with renowned institutions to further facilitate knowledge transfer and foster international research relationships.
Data Protection Information
When you apply for a position with the Technical University of Munich (TUM), you are submitting 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 in accordance with Article 13 of the General Data Protection Regulation regarding the collection and processing of personal data in the context of your application). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.