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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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health monitoring, preferably with a publication record in top-tier journals; and (c) be proficient in mainstream research frameworks for deep learning and computer vision. Applicants are invited
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-disciplinary team of researchers, including bioinformaticians, pathologists, oncologists, and computer scientists, and conduct original research on computational pathology. Digital pathology images contain rich
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in image processing, quantitative analysis, and biological interpretation Proficiency in AI/machine learning tools for image segmentation, transformation, registration, or tracking Solid mathematical
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Details Title Postdoctoral Fellow in On-Premise Computing for Autonomous Vehicles (Computer Architecture, Machine Learning and Runtime Systems) School Harvard John A. Paulson School of Engineering
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or veterinary contexts. Strong foundation in computer vision and machine learning frameworks (e.g., PyTorch, TensorFlow, OpenCV); experience with video annotation tools and datasets. Hands-on experience with
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning