Working Student – Machine Learning, Computer Vision
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Job Description
Werkstudent entwickelt Few-Shot-Computer-Vision zur kamerabasierten Speisenerkennung. menio baut modulare Software für die Betriebsgastronomie auf europäischen und internationalen Märkten.
Responsibilities:
- Research and prototype-compare current approaches to few-shot recognition and visual similarity search
- Investigate DINOv2, SigLIP, and lightweight models such as MobileNet
- Use publicly available food datasets for pretraining and benchmarking
- Build a well-documented dataset together using images from real-world cafeterias
- Develop a method for adding new dishes from 1–5 reference images without complete retraining
- Identify unknown dishes using open-set recognition
- Improve robustness to variations in portion sizes, lighting, and presentation
- Account for domain shifts between different cafeterias
- Combine few-shot recognition with detection or segmentation to identify multiple dishes individually on a tray
- Investigate how the daily menu and SigLIP’s image–text capabilities can improve recognition
- Define metrics and test scenarios, and analyze confusion cases involving similar dishes
- Work on a camera-based food recognition system for contract catering
Requirements:
- Currently pursuing a degree in Computer Science, Data Science, Mathematics, or a comparable field
- Personal projects using Python, for example as part of your studies, privately, or on GitHub
- Fundamental knowledge of machine learning and deep learning
- Interest in computer vision
- Experience with PyTorch is a plus
- Initial experience with image classification, embeddings, visual similarity search, or object detection is a plus, but not required
- Basic knowledge of or familiarity with few-shot learning, transfer learning, open-set recognition, or segmentation
- Willingness to try new approaches and learn about new topics
- Application documents submitted as a single PDF, including a cover letter, résumé, and certificates
Benefits:
- Flexible working hours and remote work
- Choice of part-time or full-time employment
- Free lunch three times a week
- Open work culture, short decision-making processes, and agile working methods
- Plenty of opportunity to make an impact
- Modern office facilities in Hanover or Plauen
- Fresh fruit, excellent coffee or tea, and a selection of beverages tailored to each location
- Regular team events and outings
- Bike leasing
- Corporate benefits
- Company pension scheme and financial support for occupational retirement provision
- Numerous professional development and training opportunities
- Attendance at international conferences
- Top-floor lounge with rooftop terrace
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