Descripción
EinleitungstextThe 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.ProjekthintergrundThe 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. Aufgaben-/StellenbeschreibungThis 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. Ihr ProfilMSc degree in computer science, machine learning, medical image computing