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, so that it can be easily used in practice (fast optimization, embedded decision-making, online updating). 1. Design a lightweight statistical/probabilistic surrogate model, integrating: • an estimation
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learning or applied mathematics. Required skills and qualities: - Fluency with Python programming for data analysis or machine learning, - Knowledge of statistical or probabilistic modelling techniques
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approaches to predict phenotypic variations from multi-source data. Develop predictive models to forecast changes in the genetic diversity of palm populations under climate change and anthropogenic pressures
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challenge: enhancing the resilience of avalanche hazard forecasting and monitoring in areas overlooking mountain roads, in a rapidly changing climate. We aim to pool procedures, snow and weather measurement
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) developing models forecasting influenza epidemics accounting for epidemic dynamics by age groups. Successful applicants will be supervised by Prof Simon Cauchemez . They will collaborate with other members