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, grounded in incentive theory (for predicting how countries would respond to incentives) and welfare economics (for normative evaluation), for determining how a reward fund should pay out its budget
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) assess future changes in these patterns under different global warming scenarios. Requirements: The successful applicant should hold a MSc or PhD degree in physics, mathematics/statistics, climate science
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of multi-species communities using theoretical modeling approaches. The candidate will focus on developing a mathematical model (e.g., preferably individual-based models) from basic equations to investigate
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theory and practice; research experiences in living labs will be an advantage Knowledge of systemic agroecology and agroecological transformation processes Experience in qualitative research methods
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The Peace Research Institute Frankfurt (PRIF) analyses the causes of international and intrastate conflicts. As one of the leading peace research institutes in Europe, we combine interdisciplinary basic research with science transfer to the public, the media and policymakers. Since 2009, the...
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The Leibniz-Institut für Wissensmedien (IWM) conducts research into the acquisition and sharing of knowledge and cooperation with digital media. The institute is a member of the Leibniz Association (Leibniz Gemeinschaft) and committed to international standards of excellent science. The IWM...
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opportunity to address a broad variety of fundamental and applied ecological questions in fields like metacommunity theory, time series analyses, multiple stressors responses, invasive species, ecological
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doctoral degree (or expected completion within a few months), for example in economics, regional science, economic/quantitative geography, mathematics or related field demonstrated knowledge and experience
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master's degree (or equivalent diploma) and a PhD in meteorology, oceanography, or a related natural or geoscientific discipline with significant physical and mathematical components. It is essential
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teaching and curriculum development. Your qualifications PhD in computer science, data science, applied mathematics, physics, or a related field. Strong expertise in machine learning and deep learning