Master Thesis - Optimizing Sparse 3D Occupancy Prediction for Embedded Devices - REF5666W
Tasks
- Design training algorithms for 3D semantic occupancy
- Implement and evaluate runtime optimized 3D perception models
- Optimize transformer models for runtime
- Prune neural networks
- Quantize CNN models
- Sparsify neural networks
- Summarize research findings in paper or master thesis
Perks/Benefits
- Coworking space
- Flexible working hours
- GPU infrastructure
- Hybrid working
- Journal clubs
- Networking events
- Occasional work from home
- Professional mentoring
Skills/Tech-stack
3D Perception | 3D Semantic Occupancy | Computer Vision | Convolutional Neural Networks | Deep learning | Docker | Git | Machine Learning | Multihead Attention | Neural Networks | Object-Oriented | Object-oriented programming | Pruning | PyTorch | Python | Quantization | Runtime optimization | Sparsity | TensorFlow | Transformer
Education
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