Master Thesis - Optimizing Sparse 3D Occupancy Prediction for Embedded Devices - REF5666W
Tasks
- Design 3D occupancy prediction models
- Evaluate model performance
- Implement training algorithms
- Optimize runtime for 3D semantic occupancy prediction
- Prune neural networks
- Quantize neural networks
- Sparsify neural networks
- Summarize research findings
Perks/Benefits
- Co-working space
- Flexible working hours
- GPU infrastructure
- Hybrid work model
- Journal clubs
- Networking events
- Remote work
- Technical mentorship
Skills/Tech-stack
3D Perception | Computer Vision | Convolutional Neural Network | Deep learning | Docker | GPU Computing | Git | Machine Learning | Model Pruning | Model Quantization | Model sparsification | Multihead Attention | Neural Network | PyTorch | Python | Quantization aware training | Runtime optimization | Transformer
Education
Bachelor of Engineering | Master of Engineering | Master of Science
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