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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 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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(e.g., tensor or multi-modality data). Work with other researchers and PhDs to develop AI applications related to image processing, computational imaging as well as computer vision. Lead a team of junior
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical technology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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(e.g., tensor or multi-modality data). Work with other researchers and PhDs to develop AI applications related to image processing, computational imaging as well as computer vision. Lead a team of junior
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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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or equivalent in a relevant subject area (physiology/neuroscience/neural engineering/physics) or near to completion of a PhD, and be experienced in a variety of quantitative research methodologies
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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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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*. * The Employment Equity Act, which is under review, uses the terminology Aboriginal peoples and visible minorities. Candidates are asked to self-declare when applying to this hiring process. City: Ottawa
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess