Overview
The Research Training Group (RTG) METEOR, a collaboration between the University of Munich (LMU) and the Technical University of Munich (TUM), is offering an exciting opportunity for aspiring researchers. This initiative is focused on the integration of machine learning and control theory, aiming to cultivate a new generation of experts in these interrelated fields. With 10 PhD and 2 postdoctoral positions available, this program is set to commence in April 2026.
Background & Relevance
Machine learning and control theory are pivotal areas in the realm of artificial intelligence, particularly in managing complex dynamical systems. The intersection of these fields allows for innovative approaches to uncertainty modeling, algorithm design, and system representation. As industries increasingly rely on sophisticated AI solutions, the demand for researchers who can navigate and innovate at this intersection is growing. This initiative not only addresses this need but also fosters collaboration between two prestigious institutions.
Key Details
- Positions Available: 10 PhD positions, 2 postdoctoral positions
- Funding: Fully funded, ranked according to the German TV-L E13 scale (100%)
- Duration: 4 years for PhD, 3 years for postdoc
- Start Date: April 2026
- Application Deadline: November 13th
- Website: METEOR RTG
- Application Link: Submit Application
Eligibility & Participation
These positions are targeted at individuals who are passionate about advancing research in machine learning and control theory. Candidates with a strong academic background in relevant fields are encouraged to apply, making this an excellent opportunity for both early-career researchers and those looking to deepen their expertise.
Submission or Application Guidelines
To apply for these positions, interested candidates should follow these steps:
1. Visit the METEOR RTG website for detailed information.
2. Prepare your application materials, ensuring they meet the requirements outlined on the site.
3. Submit your application through the provided link before the deadline of November 13th.
Additional Context / Real-World Relevance
The integration of machine learning with control theory is crucial for developing robust systems that can adapt to changing environments and uncertainties. This research is applicable across various sectors, including robotics, autonomous systems, and industrial automation. By fostering research in this area, METEOR contributes to the advancement of technologies that are essential for future innovations in AI and machine learning.
Conclusion
The METEOR initiative presents a unique opportunity for researchers to engage in cutting-edge studies at the intersection of machine learning and control theory. Interested candidates are encouraged to explore this opportunity, apply, and contribute to the evolving landscape of AI research.
Category: PhD & Postdoc Positions
Tags: machine learning, control theory, dynamical systems, LMU, TUM, DFG, uncertainty quantification, algorithm design, formal analysis
