Real-Time Endoscopic Image Enhancement Engineer

ETH Zurich; Zürich; On-site; CHF 90,000 - 130,000; Full time

Benefits offered by this job

Public transport season tickets

Car sharing

ASVZ sports program

Childcare support

Pension benefits

Job summary

ETH Zurich's Biomedical Image Computing Group seeks a candidate to implement and optimize real-time deep-learning image-enhancement models for endoscopic videos. The role focuses on translating architectures into efficient pipelines, profiling for low latency, and integrating camera data with preprocessing steps in collaboration with an industrial partner.

The position is immediately available for up to 8 months, based in Zürich, Switzerland, with emphasis on practical deployment and

Qualifications

• MSc in CS, ML, medical image computing, or a related field.
• Experience with neural-network image processing pipelines.
• Excellent Python programming skills.
• Experience with PyTorch.

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 and preprocessing pipelines.
• Contribute to project deliverables and scientific manuscript preparation.

Skills

Python

PyTorch

Real-time processing

Profiling tools

CUDA/C++

Education

MSc degree in computer science, machine learning, medical image computing, or a related field

Tools

TensorRT

ONNX

CUDA

Job description

ETH Zurich's Biomedical Image Computing Group seeks a candidate to implement and optimize real-time deep-learning image-enhancement models for endoscopic videos. The role focuses on translating architectures into efficient pipelines, profiling for low latency, and integrating camera data with preprocessing steps in collaboration with an industrial partner.

The position is immediately available for up to 8 months, based in Zürich, Switzerland, with emphasis on practical deployment and