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Computer Vision

Pubblicato il 02-09-2026 - Cosmo I Building Health Confidence in Lainate

At Cosmo, we are at the forefront of revolutionizing healthcare through our groundbreaking technology. With our innovative advancements in live endoscopy, we are transforming the landscape of medical diagnostics. We leverage AI to empower physicians, enabling them to make well-informed decisions and significantly improve the lives of their patients. Our dedication to excellence has resulted in creating GI Genius: our AI-enabled medical device for live endoscopy has received FDA approval and is successfully deployed in hospitals worldwide, making a significant impact in the field of healthcare. Check out our website for more information at a Computer Vision & AI Scientist at Cosmo MedTech AI, you will design, implement, train and validate advanced deep learning models for video-based colonoscopy, contributing to AI products already deployed in hospitals worldwide. In this role, you will collaborate closely with other AI scientists and with data, software,



and hardware teams to push the boundaries of the current state of the art and develop AI solutions that enhance real-time clinical performance and improve patient care. If you are passionate about artificial intelligence, computer vision, and healthcare, and want to see your work make a real impact in clinical practice globally, this is the perfect opportunity for you. The level of seniority for this position will be evaluated during the interview process, based on the candidate’s experience and expertise.

Pronto/a a candidarsi? Prima di farlo, si assicuri di leggere tutti i dettagli relativi a questo lavoro nella descrizione sottostante.
Key Responsibilities

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- Actively collaborate with other AI scientists and cross-functional teams to develop and implement AI solutions for medical endoscopy.
- Research, implement, train and evaluate advanced computer vision and deep learning approaches for endoscopy video analysis.
- Design rigorous experimental protocols and benchmark approaches efficiently on large-scale vide

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