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Field
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We invite applications for a Research Fellow in Computer Vision to join an exciting interdisciplinary project between the Centre for Intelligent, Sustainable Computing and the Centre for Public
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troubleshooting. Familiarity with single-cell/spatial pipelines and basic informatics (R or Python).
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without triggering autoimmunity. To address this, our research program heavily focuses on studying T and B cell population dynamics through intravital 2-photon imaging and the role of secreted immune
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necessary lead relevant meetings. To undertake any other duties relevant to the programme of research. Job Requirements: PhD degree in Computer Engineering, Computer Science, Electronics Engineering or
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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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, including design approaches, scanning methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system
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to the project. Job Requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. Min 3 years of relevant experience in computer vision, artificial intelligence, etc
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engineering, computer science, physics or related fields). A PhD* (or equivalent research experience) in a relevant subject (only for Research Fellow position). Candidates with a non-healthcare degree are
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | about 2 months ago
multidisciplinary team involving image processing labs (e.g., CMAP) and French hospitals (e.g., CHU Kremlin-Bicêtre, Hôpital Foch, Institut Mutualiste Montsouris, Institut Gustave Roussy). Phase 1: Design and develop
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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