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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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-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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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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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
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. The inventory is still ongoing, but our field is now heavily investing in the characterization of the physical and chemical properties of these objects through the use of sensitive imaging cameras and
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. Advance Bayesian and ensemble learning approaches for non-stationary temporal processes. Implement probabilistic diffusion or generative models for long-term forecasting. Collaborate closely with