Real-Time Biomedical Vision Engineer – Endoscopic Imaging

ETH Zurich; Zürich; Vor Ort; CHF 70.000 - 100.000; Vollzeit

Benefits dieser Stelle

Public transport tickets

Childcare support

Pension benefits

Zusammenfassung

ETH Zurich's Biomedical Image Computing Group seeks a MSc student or graduate to implement, optimize, and deploy deep-learning image-enhancement models for real-time endoscopic video processing. You will translate architectures into efficient pipelines linked to an industrial camera system.

The role focuses on low-latency inference, profiling tools, and adapting networks to strict timing constraints, with collaboration across BMIC and CVL and opportunities to contribute to manuscripts and

Qualifikationen

• MSc degree or equivalent in CS/ML/medical image computing or related field.
• Familiarity with neural-network-based image processing pipelines.
• Excellent Python programming skills.
• Experience with PyTorch implementations of NN architectures.
• Experience with model deployment/acceleration and camera integration is a plus.

Aufgaben

• Implement, optimize, and deploy deep-learning image-enhancement models for real-time operation.
• Translate architectures into efficient real-time pipelines connected to an industrial camera system.
• Profile and optimize models for low-latency inference.
• Adapt neural-network architectures to meet real-time constraints.
• Integrate camera acquisition and preprocessing pipelines.
• Contribute to project deliverables and manuscript preparation.

Kenntnisse

Python

Neural networks

Real-time processing

Model deployment

Ausbildung

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

Tools

PyTorch

TensorRT

ONNX

CUDA/C++

Jobbeschreibung

ETH Zurich's Biomedical Image Computing Group seeks a MSc student or graduate to implement, optimize, and deploy deep-learning image-enhancement models for real-time endoscopic video processing. You will translate architectures into efficient pipelines linked to an industrial camera system.

The role focuses on low-latency inference, profiling tools, and adapting networks to strict timing constraints, with collaboration across BMIC and CVL and opportunities to contribute to manuscripts and