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). This position focuses on the machine learning methodology of the project, aiming to: Develop probabilistic spatio-temporal models that integrate uncertainty from climate projections into land-use forecasts
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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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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting-edge deep generative probabilistic models
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Generative Modelling to apply and develop cutting-edge deep generative probabilistic models including conditional diffusion and flow matching models for synthesising Magnetic Resonance Imaging (MRI) and
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-du-Lac, Savoy. The lab is organised in three research teams: EDPs2 (partial differential equations: deterministic and probabilistic studies), Géométrie (Geometry), and LIMD (Logic, Computer science
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sensing, IoT sensors, and climate models. Design and implement deep learning models for forecasting extreme weather events such as floods, droughts, and heatwaves, integrating probabilistic approaches
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and temporal resolutions have significantly transformed the way we approach climate and weather forecasting. These technological advancements have paved the way for innovations, such as the integration
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modeling. The ideal candidate will be responsible for developing and applying probabilistic models to advance time-series analysis. Key areas of focus for this position include: 1)Probability Theory and