Internship or thesis in the field of 2D/3D Computer Vision and Deep Learning for Autonomous Flying

Are you ready to actively shape the future of autonomous aviation and work on groundbreaking AI projects? At the Fraunhofer Application Center "Connected Mobility and Infrastructure" in Ingolstadt, a unique opportunity opens up for you: Explore the potential of machine learning and computer vision to revolutionize autonomous flight systems. 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 fundamentally change aviation. 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 field of computer vision and deep learning for advanced air mobility.
Immerse yourself in innovative research fields, including:

• 2D semantic/panoptic (video) segmentation and object recognition
• 3D semantic/panoptic scene completion
• Monocular depth estimation and visual odometry
• Trustworthy AI/CV: Uncertainty estimation, out-of-distribution detection, redundancy, and local robustness
• Sensor data fusion: early, mid, and late fusion
• Vision-language models for autonomous decision-making in drone systems
• 3D reconstruction via Gaussian splatting and neural radiance fields
• (Cooperative/semantically informed) tracking and prediction of dynamic objects

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 machine learning, computer vision
• 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-00749