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trophic (phytoplankton growth and loss) variables of the Thau lagoon and the Mediterranean Sea (Station 00SETE) in an innovative way using in situ data from high-frequency automated sensors; 2) linking
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, including variational auto-encoders, generative adversarial networks (GANs) and more recently probabilistic diffusion denoising models. On the other hand the CRIStAL laboratory has a strong expertise in
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data from measurement and observation networks, as well as snow testing protocols, to provide access to a larger, harmonized information system on both sides of the border; Mutual training and
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over the course of the project. References: - Deneu B et al (2021) Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput