Mandatory Internship Applied Anomaly Detection for Multi-Channel Vision in Manufacturing

Apply now to Bosch Research and combine academic knowledge with real-world industrial this mandatory internship you will support the development of advanced anomaly detection methods for industrial multi-channel vision systems used in quality assessment in series production. Our goal is to design robust deep-learning models that leverage vision features to reliably detect defects across varying products and manufacturing scenarios.

• During the internship you will support the exploration of state-of-the-art approaches in anomaly detection and multi-channel vision and contribute to the development of generalizable models for quality assessment in industrial production.
• You will implement train and evaluate models using real production data gaining hands-on experience with industrial-scale datasets and ensuring robustness for practical deployment.
• Furthermore you will work closely with experts from Research & Development and Production units regularly sharing your progress and contributing to ongoing innovation in computer vision for manufacturing.

Qualifications :

• Education: studies in the field of Computer Science Machine Learning Artificial Intelligence or comparable with very good grades
• Experience and Knowledge: knowledge of machine-learning fundamentals; very good programming skills in Python and in one deep learning framework (PyTorch or JAX)
• Personality and Working Practice: you excel at independently tackling complex tasks with a structured analytical mindset demonstrating a strong motivation to learn and maintaining clear communication throughout the process
• Work Routine: your on-site presence is required with the possibility to work remotely part of the time
• Languages: very good in English

Additional Information :

Start: according to prior agreement
Duration: 6 months (confirmation of mandatory internship required)

Requirement for this internship is the enrollment at university. Please attach your CV transcript of records enrollment certificate examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.

Need further information about the job
Matthias Kayser (Functional Department)
 40982
Petru Tighineanu (Functional Department)
 13878

Work #LikeABosch starts here: Apply now!

#LI-DNI

Remote Work :

No

Employment Type :

Full-time