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with ecosystem service models and spatial datasets. Key Competencies Strong programming skills (Python/R/JavaScript) for tool and interface development. Ability to implement or learn GeoTOPSIS/VectorMCDA
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University of California Agriculture and Natural Resources | Oakland, California | United States | about 1 month ago
appropriate test formulas and performs computations such as multiple and partial correlation coefficients, t-tests, Chi-square, ANCOVA, linear and logistic regression, multilevel modeling, spatial analyses
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of agricultural weeds to herbicdes from an eco-evolutionary perspective. This project will develop models for the evolution of herbicide resistance that combines field data and computer models. The aim is to
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relevance to extraterrestrial environments and future energy strategies. For more information on this project, see: https://copl.ethz.ch/research/research-projects/2025-saar.html Job description The PhD
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solve research challenges and model development, as applicable. ● Contribute discrete components of a larger project under the general direction of a senior or principal researcher. ● Prepare complete
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of Geoscience and Remote Sensing , we develop advanced methods and instruments to observe and understand the Earth system. Combining satellite, airborne and ground-based measurements with modelling and machine
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for single-cell and spatial omics Deep learning and representation learning to model cellular states and interactions Explainable AI for biomarker discovery and patient stratification Cross-disease modeling
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population changes, and other demographic parameters (survival, fecundity, reproductive success, etc.). These integrated population models (IPMs) are increasingly used in ecology. They offer clear advantages
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to identify the historical, structural, and contextual drivers of disease risk and inequity, and map pathways and interactions among determinants to inform disease mapping and modelling. The RA will lead the
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driving sex-specificity in tumorigenesis, organismal development, and spatial biology of the liver. To do so, we intersect large-scale genetic analysis utilizing experimental models with spatial and single