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to classify rangeland plant species, and (2) using transfer learning to adapt deep learning models for imagery analysis to varying UAV sensors and conditions. These techniques will allow you to identify and
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potentially belowground) biomass and carbon stocks using field and remote sensing datasets. Conduct geospatial analysis and modelling using large remote sensing datasets, including optical, radar, and LiDAR
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development in fi eld ecology, data science, geospatial analysis, and scholarly communication. Responsibilities will include: Quantitative analysis and ecological modeling: Develop and apply statistical models
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make a difference in the world! Position Information The Geospatial Analyst will conduct research on remote sensing and machine learning to develop models that produce spatial data products required
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20th October 2026 Languages English English English The Department of Biology has a vacancy for a PhD Candidate in Modelling Marine Larval Dispersal and Spatial Population Genetics and Demography
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are looking for Research Data Scientist, to join our Geospatial Data Science team in the Applied Research Laboratory (ARL) at Penn State. You will aid our research efforts in this unique problem area that has
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fields: palaeoecological data synthesis and resilience to fire (Dr Jessie Woodbridge), geospatial analysis and archaeology (Prof Ralph Fyfe), palaeo-fire, FTIR and landscape modelling (Dr Michela Mariani
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: “Development of participatory models based on the integration of qualitative and quantitative spatially-referenced data for the co-design and evaluation of nature-based solutions.” Where to apply Website https
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complementary metadata such as topography, regional humidity, and climate indicators to support later knowledge representation and AI modeling phases. The resulting annotated image dataset will be hosted
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learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor crash predictions