Scientific Assistant in Real-Time Generative Models and Computer Models

pThe 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. /ph3Project background /h3pThe 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. /ppThe 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. /ppThe position is open immediately and initially limited to 8 months. /ph3Job description /h3pThis 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. /ph3Profile /h3ulliMSc degree in computer science, machine learning, medical image computing, or a related field. /liliFamiliarity with neural-network-based image processing pipelines. /liliExcellent programming skills and experience with Python. /liliExperience implementing and validating neural-network architectures using PyTorch. /liliDemonstrable experience with model deployment/acceleration (PyTorch, ONNX, TensorRT, CUDA/C++, etc.), profiling tools, and camera/sensor integration is a plus. /li /ulh3We offer /h3ulliYour job with impact: Become part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society /liliYou 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 /li /ulpWorking, teaching and research at ETH Zurich /ph3We value diversity and sustainability /h3pIn 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 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. /ph3About ETH Zürich /h3pETH Zurich is one of the world’s leading universities specialising inscience and technology. We are renowned for our excellent education,cutting-edge fundamental research and direct transfer of new knowledgeinto society. Over 30,000 people from more than 120 countries find ouruniversity to be a place that promotes independent thinking and anenvironment that inspires excellence. Located in the heart of Europe,yet forging connections all over the world, we work together todevelop solutions for the global challenges of today and tomorrow. /p #J-18808-Ljbffr