Master thesis: Advanced AI for time series prediction in automotive safety
vor 2 Monaten
What you bring to the table
- Enrolled in a Master’s program in Engineering, Physics or Mathematics.
- Prior knowledge and research experience in the field of structural mechanics, finite element simulations, and passive safety engineering is highly desirable.
- Prior knowledge and research experience in Data Analysis and Machine Learning (RNNs & Transformers) is highly desirable.
- Proficiency in programming languages such as Python and experience with deep learning frameworks (e.g. TensorFlow).
- Analytical thinking and a proactive, innovative approach to problem-solving
- Excellent communication skills and ability to work collaboratively in a team
- Reliable and independent work approach
What you can expect
- Very good supervision by experienced scientists who will support you in your work.
- Interdisciplinary collaboration with colleagues from different fields.
- Very good working atmosphere in a state-of-the-art working environment equipped with the latest technologies and resources.
- Opportunity to participate in groundbreaking research projects.
The weekly working time is 39 hours. 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 apply online with your complete application documents (cover letter, CV, certificate of enrollment, work permit if applicable)
For questions about this position, please contact:
Katriya Seitz
Recruiting
Fraunhofer Institute for High-Speed Dynamics, Ernst-Mach-Institut EMI
Requisition Number: 72964
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