Synthetic Data Engineer for Deep Learning Models
vor 2 Wochen
At Fraunhofer IPT, we're pushing the boundaries of digital production. As a Synthetic Data Engineer, you'll play a crucial role in enhancing deep learning models for production use cases. Your mission will be to design and optimize image augmentation pipelines that enrich existing datasets with synthetic images, thereby increasing the performance of deep learning models.
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
- Experimentally validate the approach using a practical use case in computer vision
- Prepare and document results
Requirements:
- Background in mechanical engineering, industrial engineering, computer science, or a related field
- Experience with Python programming
- Basic knowledge of machine and deep learning theory and methods
- Self-motivated and structured work approach
- Excellent language skills in German and/or English
What We Offer:
- 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 and infrastructure
We value diversity and promote equal opportunities. All applications are welcome, 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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