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biology, marine restoration ecology, and habitat survey techniques. Experience in habitat suitability modelling (e.g. MaxEnt, Random Forests). Experience in molecular ecology and genetic analysis
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with a PhD in climate sciences, agronomy, biogeochemistry, or functional ecology, with a strong interest in modeling. Skills in model-data comparison and relational databases would be appreciated, and
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of biodiversity - Knowledge of polar environments - Mastery of data acquisition and management tools in ecology or anthropology - Ability to work in a team Website for additional job details https://emploi.cnrs.fr
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 hours ago
. Description: Biospheric modeling --- This postdoctoral position focuses on advancing our understanding of Earth's biosphere and complex feedbacks to weather and climate through the innovative integration
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challenges. The ASE integrates earth and environmental life science, ecology, engineering and technology, human ecology, humanities, and the social sciences to address key issues of the environment and
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/ecological data analysis Experience in ecological modelling techniques (static and dynamic) using programming languages (preferably R) Experience participating in national or regional research projects
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related to staff position within a Research Infrastructure? No Offer Description The Ecological Synthesis Group (ESG), led by University Researcher Dr. Caio Graco-Roza, invites applications for a Doctoral
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seagrass data Knowledge of GIS, remote sensing or ecological modelling Experience in linking scientific research with environmental policies Scuba diving certification at the rescue diver level Ability
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are available to support conference travel and professional development. Duties & Responsibilities: • Successful candidates will develop and refine computational, mathematical, and statistical models
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, BioM will unite ecology, statistics, and philosophy to improve the modelling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key