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Master Thesis - Metric-Guided Diffusion Models for High-Fidelity In-Cabin Synthetic Data Generation(m/w/x)
Developing and evaluating metric-guided synthetic in-cabin image data for autonomous mobility at a sensor solutions firm. Master's program enrollment in AI/ML/CV and prior diffusion model exposure required. Powerful GPU infrastructure access, with journal club participation.
Requirements
- Enrollment in a master’s program in Artificial Intelligence, Machine Learning, Computer Vision, Computer Science, Robotics, Mathematics, or a related field
- Strong Python programming skills
- Solid understanding of deep learning and computer vision fundamentals
- Experience with PyTorch or similar deep learning frameworks
- Prior exposure to diffusion models, GANs, or synthetic data generation
- Experience with pose estimation, activity recognition, or multimodal models
- Familiarity with CLIP, ControlNet, or image quality evaluation metrics
- Experience using Git, Docker, and GPU-based training
- Previous research projects, internships, or open-source contributions are a plus
- Independent, structured, and goal-oriented
- Strong analytical thinkers with good problem-solving skills
- Curious and creative when exploring new ideas
- Good communicators who enjoy working in a team
- Motivated to deliver high-quality and reproducible research
- Proof of enrollment in a master’s program
- A current transcript of grades
- An excerpt from your study regulations confirming master’s thesis requirements
- Valid residence permit and work permit if not from the EU
Tasks
- Implement diffusion-based image generation models
- Experiment with image generation techniques
- Develop an evaluation pipeline for synthetic in-cabin image datasets
- Investigate metric-driven feedback loops for image quality improvement
- Analyze synthetic data's impact on computer vision tasks
- Summarize findings in a master’s thesis and potential research paper or patent
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- Git
- Docker
Benefits
Competitive Pay
- Fair remuneration for thesis work
Mentorship & Coaching
- Close supervision by experienced researchers
- Participation in journal clubs
Modern Equipment
- Access to powerful GPU infrastructure
Learning & Development
- Technical talks
Team Events
- Networking events
Other Benefits
- Diversity, Inclusion & Belonging
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Master Thesis - Metric-Guided Diffusion Models for High-Fidelity In-Cabin Synthetic Data Generation(m/w/x)
Developing and evaluating metric-guided synthetic in-cabin image data for autonomous mobility at a sensor solutions firm. Master's program enrollment in AI/ML/CV and prior diffusion model exposure required. Powerful GPU infrastructure access, with journal club participation.
Requirements
- Enrollment in a master’s program in Artificial Intelligence, Machine Learning, Computer Vision, Computer Science, Robotics, Mathematics, or a related field
- Strong Python programming skills
- Solid understanding of deep learning and computer vision fundamentals
- Experience with PyTorch or similar deep learning frameworks
- Prior exposure to diffusion models, GANs, or synthetic data generation
- Experience with pose estimation, activity recognition, or multimodal models
- Familiarity with CLIP, ControlNet, or image quality evaluation metrics
- Experience using Git, Docker, and GPU-based training
- Previous research projects, internships, or open-source contributions are a plus
- Independent, structured, and goal-oriented
- Strong analytical thinkers with good problem-solving skills
- Curious and creative when exploring new ideas
- Good communicators who enjoy working in a team
- Motivated to deliver high-quality and reproducible research
- Proof of enrollment in a master’s program
- A current transcript of grades
- An excerpt from your study regulations confirming master’s thesis requirements
- Valid residence permit and work permit if not from the EU
Tasks
- Implement diffusion-based image generation models
- Experiment with image generation techniques
- Develop an evaluation pipeline for synthetic in-cabin image datasets
- Investigate metric-driven feedback loops for image quality improvement
- Analyze synthetic data's impact on computer vision tasks
- Summarize findings in a master’s thesis and potential research paper or patent
Education
- Currently in higher education
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- Git
- Docker
Benefits
Competitive Pay
- Fair remuneration for thesis work
Mentorship & Coaching
- Close supervision by experienced researchers
- Participation in journal clubs
Modern Equipment
- Access to powerful GPU infrastructure
Learning & Development
- Technical talks
Team Events
- Networking events
Other Benefits
- Diversity, Inclusion & Belonging
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Aumovio
Industry
Automotive
Description
Das Unternehmen AUMOVIO bietet ein breites Portfolio für sichere, vernetzte und autonome Mobilität, einschließlich Sensorlösungen und Softwareexpertise.
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