Scientific Assistant – Generative Models​/Computer Vision

The Biomedical Image Computing Group develops algorithmic solutions to enable automatic interpretation and forming of biomedical images, aiming to increase efficiency and accuracy in clinical practice and research.

The enhancement of endoscopic videos is a critical yet complex task. These videos are integral to various clinical interventions, where the clarity of imaging data is essential for procedural success. Due to challenging imaging conditions, the quality of endoscopic videos can be significantly compromised by a range of artifacts. This project seeks to develop the next generation of enhancement algorithms tailored for endoscopic videos, with a focus on applications in real-time interventions.

Development efforts will prioritize both enhancement quality and computational efficiency, particularly with respect to latency.

The successful candidate will join the BMIC team at ETH Zurich and also be part of the Computer Vision Lab (CVL). The project is conducted in collaboration with an industrial implementation partner, who will provide support with hardware and software integration.

The position is open immediately and initially limited to 8 months.

This position involves the implementation, optimization, and deployment of deep-learning image-enhancement models for real-time operation. Rather than focusing primarily on model training, the successful candidate will work on translating existing and new architectures into efficient real-time processing pipelines connected directly to an industrial camera system. Typical tasks will include profiling and optimizing models for low-latency inference, implementing and adapting neural-network architectures with real-time constraints in mind, integrating camera acquisition and preprocessing pipelines, and contributing to project deliverables and scientific manuscript preparation.

• 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.

• Your job with impact:
  Become part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society
• You can expect numerous benefits, such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ, childcare and attractive pension benefits

Working, teaching and research at ETH Zurich

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