The Biomedical Image Computing Group develops algorithmic solutions for automatic interpretation and formation of biomedical images, aiming to increase efficiency and accuracy in clinical practice and research. The enhancement of endoscopic videos is a critical and complex task, essential for various clinical interventions where imaging clarity is vital. This project aims to develop next-generation enhancement algorithms for endoscopic videos, focusing on real-time applications and prioritizing both quality and computational efficiency.
Tasks
- Implement, optimize, and deploy deep-learning image-enhancement models for real-time operation.
- Translate existing and new architectures into efficient real-time processing pipelines connected to an industrial camera system.
- Profile and optimize models for low-latency inference.
- Implement and adapt neural-network architectures with real-time constraints.
- Integrate camera acquisition and preprocessing pipelines.
- Contribute to project deliverables and scientific manuscript preparation.
Requirements
- MSc degree in computer science, machine learning, medical image computing, or a related field.
- Familiarity with neural-network-based image processing pipelines.
- Excellent programming skills and experience with Python.
- Experience implementing and validating neural-network architectures using PyTorch.
- Demonstrable experience with model deployment/acceleration (PyTorch, ONNX, TensorRT, CUDA/C++, etc.), profiling tools, and camera/sensor integration is a plus.
Benefits
- Professional development support at ETH Zurich.
- Public transport season tickets and car sharing.
- Wide range of sports offered by ASVZ.
- Childcare and attractive pension benefits.
- Inclusive culture promoting equality of opportunity and diversity.
- Commitment to sustainability and a climate-neutral future.
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Das Team
The successful candidate will join the BMIC team at ETH Zurich and be part of the Computer Vision Lab (CVL), collaborating with an industrial implementation partner for hardware and software integration.