Optimizing Deep Learning Models with Synthetic Data
vor 4 Wochen
Job Description
We are seeking a highly motivated student to work on a research project focused on optimizing deep learning models for production use cases. The project involves designing and implementing customized pipelines for image augmentation to enrich existing datasets with synthetic images.
Key Responsibilities
- Conduct literature research on deep learning, image augmentation, and global optimization methods
- Identify requirements for efficient augmentation pipelines in production use cases
- Develop and implement a method for optimizing augmentation pipelines
- Experimental validation based on a practical use case in computer vision
- Prepare and document results
Requirements
- Studying mechanical engineering, industrial engineering, computer science, or a comparable subject
- First experience in Python
- Basic knowledge of machine/deep learning theory and methods
- Self-responsible and structured way of working
- Good language skills in German and/or English
About the Project
The project is part of the Fraunhofer IPT's research focus on digitalization of production systems. The goal is to increase quality, resilience, and sustainability in production by applying deep learning models to automate visual process monitoring and quality control.
What You Will Gain
- Scientific work on a current and practice-relevant topic
- Professional supervision and support for your thesis
- Participation in innovative research and industry projects with industry partners
- Access to state-of-the-art machine park equipped with edge cloud systems and 5G infrastructure
We value and promote the diversity of our employees' skills and 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.
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