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co-funded by YCNCC and Spark Climate Solutions . The goal of the position is to advance our understanding of warming-induced feedbacks on emissions of greenhouse gases (specifically, methane) from
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to its state-of-the-art infrastructure. With an innovative approach, UM6P places research and innovation at the heart of its educational project as a driving force of a business model. In its research
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provision—to the scientific community—of observations, data, and models for the geosciences and the environment. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR5563-OLIVAN-006/Default.aspx
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of their development and structure. Working closely with colleagues at the Universities of Leeds and Reading, you will integrate theoretical understanding, observational data, and modelling approaches to improve
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to solutions with clear societal and policy impact. Cyprus also offers a high quality of life, with a safe environment, mild climate, and easy access to both European and international travel. Centre / Unit
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some of today’s most pressing societal challenges, including biodiversity loss, climate change, and environmental pollution. Our research combines advanced spatio-temporal modelling, Bayesian inference
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together to develop solutions for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/phd-position-in-modeling-and-c… Requirements Research
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University of Natural Resources and Life Sciences, Vienna (BOKU) | Vienna, Virginia | United States | about 2 months ago
is one of Europe’s leading life science universities. The Department of Landscape, Water and Infrastructure (LAWI, https://boku.ac.at/en/lawi ) stands for interdisciplinary and sustainable research and
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research group focused on understanding the pathophysiology of arthritis. Our work integrates in vitro models across diverse cell types and in vivo models of joint injury and inflammatory arthritis
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climate. The doctoral researcher will be working on the AI-Dream project. They will have to develop criteria and evaluation suites to test the “physical” accuracy of deep Machine Learning (ML) models