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- Enthusiasm and interest in disease ecology - Experience with mathematical disease modelling, spatial and/or niche modeling, meta-analyses, and other quantitative analyses (especially if requesting remote
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to mastering the great challenges facing society today. The Institute of Resource Ecology performs research to protect humans and the environment from hazards caused by pollutants resulting from technical
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to the vagaries of ocean basins, particularly in African ocean basins. The doctoral student will be required to: - Anticipate and understand coastal and marine socio-ecological tipping points at different scales in
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The proposed PhD project is part of the PEPR FORESTT, a national interdisciplinary research program aimed at studying the socio-ecological transition of forest systems. The targeted MONITOR project within
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/regional/spatial economics; behavioural economics; Economic geography; Computational Science; Geo-information Science, Complex systems engineering and management, Industrial Ecology or[KL1] a similar
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Productivity Index (RPI) using observed versus potential productivity modelled with machine learning (https://doi.org/10.1016/j.ecolind.2025.113208 ), this applied geospatial ecology project will study how
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today and in the future. For more information: http://www.slu.se/en/departments/forest-ecology-management/ Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about
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-derived spectral signatures. Analytical workflows will be implemented in Python, R, Matlab and QGIS. Classification outputs will be integrated with ecological and spatial data to evaluate biogeographical
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for coastal ecosystems. You will perform statistical analyses of time series and spatial data on ecosystems and human activities and participate in more holistic analyses of socio-ecosystems. You will also
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning