Internship or thesis (Bachelor/Master) in the field of Reinforcement Learning

Are you ready to develop and implement the latest Reinforcement Learning approaches? At the Fraunhofer Application Center for "Connected Mobility and Infrastructure" in Ingolstadt, a unique opportunity opens up for you: Explore the potential of Reinforcement Learning to address challenges in the fields of autonomous aviation, traffic optimization, or fundamental research. In close collaboration with leading industry partners, you will ensure that your research results can be translated into practice, making a real difference. This position offers you the chance to actively contribute to groundbreaking technologies and to implement cutting-edge research approaches, particularly in the areas of Multi-Agent Reinforcement Learning and Hierarchical Reinforcement Learning. Seize the opportunity to work on highly relevant and practical research projects and experience interdisciplinary research at the forefront of technological innovation. With access to state-of-the-art computing resources, simulations, and datasets, you can fully unleash your ideas. We provide you with specialist supervision at the highest level to best support your personal and professional development.

What you will do

As an intern or as part of your thesis, you will become part of our dedicated team and work with state-of-the-art technology on exciting projects in the fields of autonomous aviation or traffic optimization. Apply your own ideas and immerse yourself in innovative research fields, including:

• Traffic optimization using multi-agent Reinforcement Learning and hierarchical Reinforcement Learning
• Reinforcement Learning-based recommendation systems for traffic optimization
• Bridging quantum technology and Reinforcement Learning
• Reinforcement Learning for path and trajectory planning in aerospace systems
• Vision-language-action models for autonomous drone systems
• Multi-agent cooperation using Reinforcement Learning and control barrier functions
• Mission planning for aerospace systems using evolutionary algorithms and Reinforcement Learning
• Single-shot Reinforcement Learning

What you bring to the table

• Enrolled in one of the following or related fields of study (Bachelor/Master): Computer Science, Data Science, Mathematics, Physics, Electrical and Information Engineering, mechatronics or a related subject area
• Very good academic performance
• Experience from previous own research work or courses in Reinforcement Learning and optimization
• Experience in path planning / trajectory planning or traffic optimization is desirable
• Knowledge of programming languages such as Python and experience with deep learning frameworks (e. g. PyTorch)
• Passion for research and solving complex problems
• Structured, independent and results-oriented way of working
• Excellent communication skills and ability to work in a team

What you can expect

• Challenging tasks in cutting-edge and application-relevant subject areas
• Interdisciplinary research on promising technologies
• Access to state-of-the-art computing resources and a high-performance infrastructure
• Professional supervision
• Flexible working hours

The weekly working time is 39 hours. This position is also available on a part-time basis. We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

Interested? Apply online now. We look forward to getting to know you!

Please include a short motivation letter with your application and specify your desired topic for the thesis. Our proposed topic doesn't quite match your interests and skills? No problem! Feel free to suggest your own idea, and together we'll find the best fit.

If you have any questions, please contact: bewerbung.studenten@ivi.fraunhofer.de
You can find more information on the institute online: www.ivi.fraunhofer.de/en

Fraunhofer Institute for Transportation and Infrastructure Systems IVI 

www.ivi.fraunhofer.de 

Requisition Number: IVI-Hiwi-00750