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(https://www.polito.it/en/education/phd-programmes-and-postgraduate-school ) Desired skills: Knowledge in EcoDesign, climate sciences and life-cycle assessments Project management Knowledge of the host
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, hydrological, socio-economic, and migration data; •predictive modeling and forecasting of migration phenomena based on environmental and climate variables; •development and application of dynamic systems models
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Hydrology and Climate Impacts has strong expertise in water resources, extreme value analyses, hydrological modelling, and climate impact assessments and is looking, from spring/summer 2026 for two years
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of risk models and decision-support tools for environmental health and climate resilience. Working closely with the PI to develop new research ideas, and to write up and present project proposals
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 29 days ago
: the Center for Climate Leadership and AI-driven Integrity in Mitigation. CLAIM advances the credible use of artificial intelligence, especially generative AI and large language models (LLMs), to accelerate
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stressors such as climate change. Both researchers with a more clinically oriented profile (e.g., research with patient groups) and researchers with a more fundamental profile (e.g., laboratory research with
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for the mitigation of air pollution and climate change. This research supports the activities of the Climate and Clean Air Coalition (CCAC http://www.ccacoalition.org ), the World Economic Forum and the Clean Air
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therefore collaborate with colleagues in climate science, systems modelling, and policy analysis. Where to apply Website https://www.academictransfer.com/en/jobs/356362/phd-integrated-assessment-model
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of innovation diffusion, coupled Earth and social system modeling, and/or climate change adaptation. The position will start in the summer/fall semester of 2026 under the supervision of Dr. Nicholas Magliocca
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools