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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
Contract. General Positioning Artificial intelligence (AI) began revolutionizing digital services in 2013 with its application to image processing and computer vision. A second revolution arrived around 2020
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 3 months ago
introduces students to the genetic variation between and within populations. The topics include evolutionary forces, quantitative genetics, and Bayesian statistics as it applies to forensic biology.[24L/12S
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software engineering practices. Desirable Skills: knowledge of computer vision, Bayesian theory, or signal processing; experience with prototyping, experimentation, or technical evaluation of interactive
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
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29 Jul 2026 Job Information Organisation/Company UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Department Department of Computer Engineering Research Field Computer science » Other Researcher Profile
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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. This large multimodal dataset allows us to estimate and test different computational models of the decision and learning processes. One postdoc is currently working on the MEG and iEEG data, and one PhD
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, optimization, and characterization integrating imaging, experimental metadata, and diffraction outcomes. Design and deploy computer vision methods to detect and track crystal growth. Develop closed-loop
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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific