As a global semiconductor leader in power systems and IoT, we enable game-changing solutions for green and efficient energy, clean and safe mobility, as well as smart and secure IoT. That's why you probably use our products every day: smartphone, charger, electric toothbrush, coffee machine, refrigerator, remote control and much more. We are looking forward to your application!
1. Take responsibility: You will develop a foundation model that can be easily customized and fine-tuned for various vision tasks using Infineon's dataset
2. Data is everything: You work on a model which should be capable of accommodating images of different sizes and spatial resolutions, effectively utilizing the spectral information, and outperforming existing models for downstream tasks
3. Expand your horizons: You will deal with Infineon's unique dataset, which differs significantly from standard image datasets
4. Identify needs: You optimize the model to effectively utilize spectral information, potentially drawing inspiration from models like DINOv2, Mask Auto Encoder (MAE), IJEPA
5. Experience research: You investigate multi-stage pretraining techniques to improve model performance
6. Reliable work: You validate the model's performance through ablation studies and comparison with existing foundation models
7. Study field: You are currently studying Data Science, Electrical Engineering, Informatics or a related subject
8. Experience: You are familiar with foundation models, such as Segment Anything and Masked Auto Encoder (MAE). IJEPA, DIN
9. Personality: You have excellent communication and presentation skills as well as strong problem-solving and analytical skills
10. Skills: You are proficient in programming languages, such as Python, deep learning frameworks, such as TensorFlow or PyTorch and in Bash scripting, Linux terminal knowledge to streamline data processing; model training would be advantageous
11. Language skills: You have good English skills, both written and spoken
Please attach the following documents to your application:
12. CV in English
13. Certificate of enrollment at university
14. Latest grades transcript (not older than 6 months)
15. High school report
Important information:
16. Working part-time: The focus is on studies. Therefore, working student is possible during the lecture period with a maximum of 20 hours per week.
17. Proper students (according to the German law) are welcome: You must be enrolled and the examination results or modules of your studies must not have been completed yet, so that you can still work in our team for at least 6 months. You must also not be in a semester of leave.
18. You should live close to the site: It is important for us to work with you on site and to integrate you into the team. You should therefore be able to come to the site regularly.
Benefits
19. Coaching, mentoring networking possibilities
20. Wide range of training offers & planning of career development
21. International assignments
22. Different career paths: Project Management, Technical Ladder, Management & Individual Contributor
23. Flexible working conditions
24. Home office options
25. Part-time work possible (also during parental leave)
26. Sabbatical
27. Holiday child care
28. On-site social counselling and works doctor
29. Health promotion programs
30. On-site canteen
31. Private insurance offers
32. Wage payment in case of sick leave
33. Corporate pension benefits
34. Flexible transition into retirement
35. Performance bonus
36. Accessibility, access for wheelchairs
37. Possibility to work remotely from abroad (EU)