Master Thesis - Computer Vision for Liquid Phase Transmission - Forschungszentrum Julich GmbH P-VA Personalabrechnung

Key Responsibilities

• Design and implement computer vision pipelines to capture and analyze liquid flow in electronic assemblies.
• Develop machine learning models (e.g., CNNs) for defect detection and process monitoring.
• Process and annotate large image datasets, ensuring data quality and consistency.
• Collaborate with experimental teams to integrate imaging hardware and validate results.
• Document methodology, results, and publish findings in technical reports and conferences.

Requirements

• Strong background in computer vision, image processing, and machine learning.
• Proficiency in Python, OpenCV, and deep learning frameworks such as TensorFlow or PyTorch.
• Experience with data annotation, model training, and performance evaluation.
• Excellent analytical skills and ability to translate research into practical solutions.
• Good communication skills for presenting results to multidisciplinary teams.