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Education (MOE) aims to improve the measurement forest structure and aboveground biomass by linking these remote sensing observations to ground measurements of representative ecosystems in Southeast Asia
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: Experience with remote sensing analysis, geographic information system, or flood mapping and analysis. Experience with geospatial data management. Knowledge of cloud-based applications. Preferred
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Your Job: You will join the Simulation and Data Lab `AI and ML for Remote Sensing,` which aims to enhance visibility in interdisciplinary research between applications from remote sensing and large
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activity, and coastal protection benefits in the Caribbean. The postdoc will lead the development and application of a modeling framework to integrate remotely sensed datasets with coastal hydrodynamic
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Simulation and Data Lab `AI and ML for Remote Sensing,` which aims to enhance visibility in interdisciplinary research between applications from remote sensing and large-scale AI with high-performance and
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in the Arctic and Antarctica using remote sensing, laboratory measurements, and field data. The candidate will be based at OSU, but will have opportunities to work directly with co-investigators
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letter; e) Knowledge of quantitative (including modelling) and qualitative research methodologies, remote sensing and GIS techniques and long-term ecological data analysis - information provided in the CV
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knowledge of these areas of research is essential, and a strong background in remote monitoring in epilepsy is expected. It is essential the role holder has completed training in neurology and is working
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to digital twin, intelligent infrastructure, remote sensing, human-data interaction, data analytics, machine learning, or mixed reality. Articulated potential for teaching to contribute to undergraduate and
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available from 01/04/2024 to 31/03/2025. The successful applicant will combine remotely sensed datasets with machine learning to map peat extents, habitats, habitat condition and change. The post will include