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Field
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. Experience with Python, SQL, and common libraries/frameworks for data analysis, workflow automation, and image or geospatial data processing. Experience with relational databases, data modeling, ETL pipelines
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
these engagements will be integrated with outputs from advanced AI models (e.g.,geospatial deep learning, graph neural networks, and explainable AI ) to refine the AMR risk maps and ensure local relevance. Required
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and illegal logging detection. Working at the intersection of computer vision, deep learning, geospatial analysis and remote sensing, the postholder will collaborate closely with academic and
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Department of Land Surveying and Geospatial Science Research Assistant (Ref. 260918005) [Appointment period: twelve months] Duties The appointee will assist the project leader in the research
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formulate research questions at the intersection of AI, EO and advanced computing Develop and investigate AI methods for multisource EO data, exploring approaches such as geospatial foundation models and
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develop and validate machine learning models using systematically collected and accurately annotated GPR datasets. By combining geospatial data, subsurface sensing, and AI, you will contribute to the next
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Department of Land Surveying and Geospatial Science Research Assistant (Ref. 260818003) [Appointment period: seven months] Duties The appointee will assist the project leader in the research project
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, environmental modelling and geospatial data science. The position is part of Vertical Africa (VERTICAF), an interdisciplinary research project funded by the Swiss National Science Foundation (SNSF). We
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the interactions between crop growth and environmental factors, and develop geospatial information-based models for environmental effects. Support the development of multidimensional “genotype–environment–phenotype
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: Department of Biology (Professor Laura Pollock ) Position Summary: Develops and implements species distribution models (SDMs) to predict the geographic distributions of tropical plant species using