Scientific Assistant in Real-Time Generative Models and Computer Models

Academic Positions; Schweiz; Presencial; CHF 73.000 - 95.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Impactful research

Industry collaboration

Descripción de la vacante

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.

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Python

Neural networks

Image processing

Profiling tools

Camera integration

Educación

MSc degree in computer science / ML / medical image computing

Herramientas

PyTorch

ONNX

TensorRT

CUDA/C++

Descripción del empleo

Organisation/Company ETH Zürich Research Field Computer science » Other Engineering » Biomedical engineering Engineering » Other Medical sciences » Other Researcher Profile First Stage Researcher (R1) Application Deadline 18 Jan 2027 - 22:59 (UTC) Country Switzerland Type of Contract Temporary Job Status Part-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Scientific Assistant in Real-Time Generative Models and Computer Models

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.

Project background

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.

Profile

• 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.

We offer

• 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

Working, teaching and research at ETH Zurich We value diversity and sustainabilityIn line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future .Curious? So are we.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

About ETH ZürichETH Zurich is one of the world’s leading universities specialising in
science and technology. We are renowned for our excellent education,
cutting-edge fundamental research and direct transfer of new knowledge
into society. Over 30,000 people from more than 120 countries find our
university to be a place that promotes independent thinking and an
environment that inspires excellence. Located in the heart of Europe,
yet forging connections all over the world, we work together to
develop solutions for the global challenges of today and tomorrow.