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AI Research for Climate Change and Environmental Sustainability

Accurately predicting the evolution of climate worldwide and anticipating extreme weather events is a challenge, particularly due to the heterogeneity of the data collected in the field and the abundance of data generated by computer models. Artificial intelligence and machine learning could well assist scientists, communities, energy utilities, and decision makers in confronting the climate crisis. With this in mind, the Inria Centre in Paris is planning to set up a new team, headed by Claire Monteleoni, dedicated to AI research to combat climate change. Let's take a closer look at what they’ll be exploring. ....

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Michigan Tech University Senate considers new programs | News, Sports, Jobs

The Michigan Technological University Senate elected new officers at its biweekly meeting Wednesday evening and discussed a nursing program that is being launch ....

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UK Green Business Awards - Finalists Announced

The shortlist for the inaugural UK Green Business Awards has been unveiled and tickets are now available for the glittering awards ceremony on the evening of Thursday 29th June ....

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"Environmental data science: Part 2" by Wesley S. Burr, Nathaniel K. Newlands et al.

Environmental data science is a multi-disciplinary and mature field of research at the interface of statistics, machine learning, information technology, climate and environmental science. The two-part special issue ‘Environmental Data Science’ comprises a set of research articles and opinion pieces led by statisticians who are at the forefront of the field. This editorial identifies and discusses common research themes that appear in the contributions to Part 2, which focuses on applications. These include spatio-temporal modeling; the problem of aggregation and sparse sampling; the importance of community-building and training for the next generation of specialists in environmental data science; and the need to look forward at the challenges that lie ahead for the discipline. This editorial complements that of Part 1, which largely focuses on statistical methodology; see Zammit-Mangion, Newlands, and Burr (2023). ....

Environmental Data Science , Patio Temporal ,

"Environmental data science: Part 1" by Andrew Zammit-Mangion, Nathaniel K. Newlands et al.

Environmental data science is a multi-disciplinary and mature field of research at the interface of statistics, machine learning, information technology, climate and environmental science. The two-part special issue ‘Environmental Data Science’ comprises a set of research articles and opinion pieces led by statisticians who are at the forefront of the field. This editorial identifies and discusses common strands of research that appear in the contributions to Part 1, which largely focus on statistical methodology. These include temporal, spatial and spatio-temporal modeling; statistical computing; machine learning and artificial intelligence; and the critical question of decision-making in the presence of uncertainty. This editorial complements that of Part 2, which largely focuses on applications; see Burr, Newlands, and Zammit-Mangion (2023). ....

Environmental Data Science , Patio Temporal , Statistical Computing ,