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features. Build and test pipelines for pose detection, object tracking, optical-flow analysis, and gaze–scene alignment, in collaboration with computer vision researchers. Analyze large multimodal datasets
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Gravitational-Wave Astronomy Using Artificial Intelligence, to work on computational Bayesian inference methods and their astrophysical applications. Southampton's School of Mathematical Sciences is home to a
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, and hybrid models integrating computer-vision–derived features. Build and test pipelines for pose detection, object tracking, optical-flow analysis, and gaze–scene alignment, in collaboration with
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. Automated data-driven selection procedures will enable to gain objectivity and capacity to handle large amount of data from a wide range of epidemics. The first challenge consist in refining previous models
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tasks require high-frequency evaluations of forward models, in order to quantify the uncertainties of rock and fluid properties in the subsurface formations. Therefore, the objectives of this research
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-objective, real-time) and supply-chain optimization; PdM and RUL with health monitoring; digital twins/smart factories, cross-site transfer and federated/edge learning; uncertainty estimation and calibration
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 19 hours ago
strategies. arXiv preprint arXiv:2502.19308, 2025 Objectives The goal of the postdoc project is to develop a robust and flexible interface between crop models and reinforcement learning (RL) to enable decision
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of a mentor, the participant will use a range of phylogenetic methods (including Bayesian) to study how interspecies transmission, genomic reassortment, and farm production practices affect the evolution
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uncertainty from climate projections into land-use forecasts. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models
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, and model evaluation. An understanding of epidemiologic principles, arboviral transmission dynamics, MCMC and Bayesian modeling, and prevention/intervention design. An understanding of data acquisition