Real-Time Medical Vision Engineer for Endoscopy

Academic Positions; Schweiz; On-site; CHF 73,000 - 95,000; Part time

Benefits offered by this job

Impactful research

Industry collaboration

Job summary

ETH Zürich's Biomedical Image Computing Group seeks a Scientific Assistant in Real-Time Generative Models and Computer Models to develop real-time endoscopic video enhancement pipelines. You will work on translating neural architectures into efficient processing with camera integration and low latency requirements.

The role focuses on implementing and deploying models for real-time clinical applications, collaborating with the CVL and industry partner, and contributing to publications.

Qualifications

• 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.

Responsibilities

• Implement, optimize and deploy deep-learning image-enhancement models for real-time operation.
• Translate architectures into efficient real-time processing pipelines connected to an industrial camera system.
• Profile and optimize models for low-latency inference and integrate camera acquisition.

Skills

Python

Neural networks

Image processing

Profiling tools

Camera integration

Education

MSc degree in computer science / ML / medical image computing

Tools

PyTorch

ONNX

TensorRT

CUDA/C++

Job description

ETH Zürich's Biomedical Image Computing Group seeks a Scientific Assistant in Real-Time Generative Models and Computer Models to develop real-time endoscopic video enhancement pipelines. You will work on translating neural architectures into efficient processing with camera integration and low latency requirements.

The role focuses on implementing and deploying models for real-time clinical applications, collaborating with the CVL and industry partner, and contributing to publications.