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Master Thesis - Optimizing Sparse 3D Occupancy Prediction for Embedded Devices(m/w/x)
Optimizing sparse 3D occupancy prediction models for embedded devices. Deep learning, Python, and runtime optimization experience required. Hybrid work, flexible hours, and specialist support.
Requirements
- Study in AI, ML, Computer Vision, CS, Robotics, Math or comparable
- Strong Python programming skills
- Strong interest in runtime optimization
- Experience in 3D perception, multihead attention, deep learning, large architecture training/optimization, Docker, PyTorch, OOP, Git
- Research/software engineering experience (internships, work, competitions, projects, papers)
- Fluent English language skills
- Independent, goal-oriented, structured work with high-quality results
- Analytical thinking and problem-solving skills
- Creativity and flexibility
- Very good communication and teamwork skills
- Current enrollment certificate, grade level, thesis regulations excerpt
- Valid residence and work permit (non-EU)
- Applications from severely handicapped people welcome
Tasks
- Work on sparsifying, pruning, and quantizing CNN and transformer-based architectures
- Design and implement models for runtime-optimized 3D semantic occupancy predictions
- Evaluate models and training algorithms
- Summarize research findings in a paper or master thesis
- Prepare findings for potential patent submission
Education
- Currently in higher education
Languages
- English – Fluent
Tools & Technologies
- Python
- Docker
- PyTorch
- Git
Benefits
Mentorship & Coaching
- Qualified support from specialists and PhDs
Flexible Working
- Flexible working hours
- Hybrid working model
- Occasional work from home
Modern Equipment
- Powerful GPU infrastructure
Learning & Development
- Journal clubs
Team Events
- Networking events
Diverse Work
- Technical topic sharing
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Master Thesis - Optimizing Sparse 3D Occupancy Prediction for Embedded Devices(m/w/x)
Optimizing sparse 3D occupancy prediction models for embedded devices. Deep learning, Python, and runtime optimization experience required. Hybrid work, flexible hours, and specialist support.
Requirements
- Study in AI, ML, Computer Vision, CS, Robotics, Math or comparable
- Strong Python programming skills
- Strong interest in runtime optimization
- Experience in 3D perception, multihead attention, deep learning, large architecture training/optimization, Docker, PyTorch, OOP, Git
- Research/software engineering experience (internships, work, competitions, projects, papers)
- Fluent English language skills
- Independent, goal-oriented, structured work with high-quality results
- Analytical thinking and problem-solving skills
- Creativity and flexibility
- Very good communication and teamwork skills
- Current enrollment certificate, grade level, thesis regulations excerpt
- Valid residence and work permit (non-EU)
- Applications from severely handicapped people welcome
Tasks
- Work on sparsifying, pruning, and quantizing CNN and transformer-based architectures
- Design and implement models for runtime-optimized 3D semantic occupancy predictions
- Evaluate models and training algorithms
- Summarize research findings in a paper or master thesis
- Prepare findings for potential patent submission
Education
- Currently in higher education
Languages
- English – Fluent
Tools & Technologies
- Python
- Docker
- PyTorch
- Git
Benefits
Mentorship & Coaching
- Qualified support from specialists and PhDs
Flexible Working
- Flexible working hours
- Hybrid working model
- Occasional work from home
Modern Equipment
- Powerful GPU infrastructure
Learning & Development
- Journal clubs
Team Events
- Networking events
Diverse Work
- Technical topic sharing
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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