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PRISM

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Cerrado

Preservation and RecognItion of Spatial patterns using Machine learning

Ecological research necessitates the production of spatial maps representing an array of critical variables such as biodiversity, climate, land cover, and soil carbon storage. While current machine learning methods, such as the we... ver más

13/08/2026

Presupuesto desconocido

Líder del proyecto

UNIVERSITAET MUENSTER No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-08-14

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023 ExpectedOutcome:Project results are expected to contribute to the following outcomes:

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Duración del proyecto: 23 meses Fecha Inicio: 2024-08-14
Fecha Fin: 2026-08-13

Líder del proyecto

UNIVERSITAET MUENSTER No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Ecological research necessitates the production of spatial maps representing an array of critical variables such as biodiversity, climate, land cover, and soil carbon storage. While current machine learning methods, such as the well-known random forest (RF), have been effective in generating maps for these variables, they often overlook the intricate spatial patterns inherent in ecological processes. The PRISM project seeks to introduce a novel approach that addresses this limitation by integrating and validating spatial patterns within machine learning models. The project will draw upon insights from various fields, including geography, landscape ecology, statistics, and computer science. To ensure the widespread dissemination and accessibility of its findings, PRISM will adopt a comprehensive open science approach, including manuscript publications, the development of open-source software, and the sharing of repositories containing data and code, enabling others to reproduce and build upon the project's results. Through this project, an exchange of knowledge is anticipated between the researcher and the host institution, fostering a collaborative partnership. Under the supervisor's mentorship, the researcher will acquire essential skills in group organization, grant preparation, and research leadership. The researcher will enrich the host institution by creating innovative methods for spatial data analysis, implementing impactful teaching methodologies, and sharing the principles of open science. Ultimately, the PRISM project is poised to fuel the researcher's interdisciplinary growth, positioning him as a valuable asset in both academia and industry. The project's outcomes have the potential to improve how ecological research is conducted, leading to more accurate predictions and a deeper understanding of complex spatial patterns in ecological systems.

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