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Student or Scientific Assistant for Remote Sensing Data Processing and Cloud-based Workflows (f/m/d)
at different spatial scales (from plot level to regional level). These indicators are derived using different sensors (field spectrometer, drone with hyperspectral sensor, and satellite imagery) that can be a
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, Physics , quant-ph , Quantum Science + Quantum Information Science + Quantum Optics + Theoretical Physics , Quantum Sensors , Theoretical Particle Physics , Theoretical Physics, HEP-Phenomenology (hep-ph
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. Preferred Qualifications Experience with energy and air quality sensor technology, data, and modeling platforms. Equipment Utilized Software Skills: Most or all: R, STATA, Python, MATLAB, SQL, ArcGIS, QGIS
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or predictive modelling, edge AI, AI for biomaterials formulation, processing and manufacturing optimization. Wearable devices – wearable physiological sensors, smart textiles, soft robotics, and exoskeletons
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trained in and ultimately read and interpret proprietary building automation system dashboards (e.g. Johnson Controls), respond to equipment sensors and alarms that send data and readings to a dashboard
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
focused on environmental applications for air quality management. This will include acquiring datasets related to multiple air quality sensors from diverse data sources, develop open-source, easy to use
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of Norway through the “Arctic MSCA-PF program”. Successful postdoc candidates will be invited for a two-day MSCA-PF symposium June 10-11, 2026 (hybrid physical/digital event, see details at https://uit.no
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Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 1 month ago
Change and Transformation Processes," and "Artificial Intelligence and Sensor Technology" - it combines its strengths in teaching and research across institutes and faculties. At its locations in Cottbus
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, including renewable energy sources and energy storage systems.Development of predictive models and soft sensors for monitoring the technical condition and operational parameters of energy infrastructure (e.g
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maintain end-to-end EO data processing pipelines, from sensor calibration to the extraction of geophysical variables. Implement machine learning and image processing techniques to fuse optical and SAR data