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Machine learning to decipher plankton big data and unravel the link between marine plankton diversity and associated ecosystem services

Objetivo

"Marine plankton is highly diverse, with millions of taxa and genes. This huge biodiversity plays a key role in providing ecosystem services to human societies, such as oxygen production, carbon export, climate regulation, or food provision. Large datasets of various types have been gathered to document marine plankton, from individuals (e.g. species occurrences, individual images) to communities (acoustic, satellite, metagenomics). Yet, these plankton ""big data"" are largely underexploited, while new tools from machine learning (ML) are now available to mine them. Today, the two main challenges of using ML for plankton big data are: i) to handle, combine, and decipher data of various types, and ii) to use these data to improve marine ecosystem models, especially the ones used by the IPCC and IPBES to anticipate the impact of climate change on our oceans. Hence, there is a crucial need for training a new generation of innovative doctoral researchers (DRs) at the interface of key scientific disciplines such as marine ecology, omics, imaging, satellite, acoustics, numerical modelling, climate sciences and data science, including machine learning. We offer a unique international, multidisciplinary, and multisectoral high quality and innovative research and training network. It gathers European world-class research and training centres, non-governmental organisations, governmental organisations, small and medium enterprises (SMEs), and multinational companies from 6 European countries plus Canada. The non-academic partners bring expertise in plankton harvesting, fisheries management, biodiversity conservation, marine policy, biotechnologies, computer vision, data management, communication, and public engagement. The unique set of skills of our DRs will advance their careers in various sectors such as government and policy, academic research, private consultancy, SMEs or industry, including information and communication technology, and big data analytics."

Ámbito científico (EuroSciVoc)

Palabras clave

Programa(s)

Tema(s)

Régimen de financiación

HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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Convocatoria de propuestas

(se abrirá en una nueva ventana) HORIZON-MSCA-2025-DN-01

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Coordinador

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Aportación neta de la UEn
€ 314 669,16
Coste total
Sin datos

Participantes (12)

Socios (11)