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FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria - A PhD in mathematics/statistics/AI applied to ecological issues. - A strong publication record. - Proficiency in R and Python
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and experience: Essential criteria PhD in applied mathematics, statistics, engineering, computational biology, econometrics, or a related discipline. Experience in developing complex models using real
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others, pseudospectra theory, and empirical data from DRAGNet and global demographic databases (COMPADRE, COMADRE, PADRINO). The project combines mathematical modelling, simulation, and empirical synthesis
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for mapping processes. Moreover, the successful candidate will support other activities in WP2 by integrating the models into AI-based perception systems, contributing to creating multi-modal ecological maps
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fertilization). The work aims to identify the key ecological factors influencing the adoption of crop rotation in agricultural systems. Key responsibilities: Model crop rotations in space and time: development
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of organic soils, and process modelling. The position is available immediately, with a start date as soon as possible. Your qualities a PhD degree in a relevant discipline, such as environmental sciences
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candidate with a PhD in ecology, ecotoxicology, or environmental modeling, and strong analytical and programming skills (e.g., R or Python). Experience with biodiversity metrics, ecosystem service modeling
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The mission of the Leibniz Centre for Agricultural Landscape Research (ZALF) as a nationally and internationally active research institute is to deliver solutions for an ecologically, economically
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, responsible, and organized postdoctoral researcher to develop mathematical models of adaptive stress regulation. These models will inform empirical research with humans. Join our team! We invite applications
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Minimum qualifications include a PhD in quantitative ecology, fisheries, statistics, applied mathematics, theoretical ecology, or a related field. Certifications/Licenses Required Knowledge, Skills, and