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Field
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and field monitoring work performed by a PhD student at LIST and other researchers in LAFI, and extend the existing Vegetation Optimality Model (VOM, https://vom.readthedocs.io ) to test the following
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, systemic nature of climate adaptation sits uneasily with existing cognitive frames, project-based management cultures, and competing organizational transitions like digitalization and the energy transition
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theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 5 hours ago
(GMAO), is a comprehensive Earth system model and data assimilation system. The GMAO assimilates aerosol optical depth (AOD) observations from space-borne (e.g. MODIS, VIIRS) and ground-based (AERONET
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discrete black box combinatorial optimization problems (https://arxiv.org/abs/2510.01824 ). In this work, we parameterize a multivariate autoregressive generative model for generating solutions. By sampling
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/modelling tools; Show a strong publication record in peer-reviewed journals commensurate with career stage; and Possess excellent command of written and spoken English; proficiency in Cantonese and/or
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lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based
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theory, modeling, and AI-assisted optimization activities within the consortium. Reporting and dissemination of the results. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1074
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candidate will lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based
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to leverage machine learning approaches for the optimization of polymer properties and degradation profiles. The successful candidate will lead pioneering research in controlled polymer synthesis, employing