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experimental and observational studies (e.g. using field, lab, remote sensing data) along environmental gradients to increase understanding of how different types of forest management (e.g. continuous cover
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for remote working. Eligibility criteria The candidates must provide a document issued by the doctoral school they have been enrolled to, certifying the PhD candidate status. The candidates have to prove the
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at the Radboudumc, Radboud University and Donders Graduate School. finalize the project with a PhD thesis. Where to apply Website https://www.academictransfer.com/en/jobs/356184/phd-candidate-digital-biomarker
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transfer. Fire observation systems. Interpretation of remote sensing data. Feather characterization. Modeling and automation of workflows. Robust processing of field measurements. Professional Experience
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research, optimization, network programming and routing Optical metrology for surface texturing and imaging Remote sensing and photogrammetry Drone mapping Surveying with total station LiDAR and Optical
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Nursing (MSN), Doctor of Nursing Practice (DNP), and PhD in Nursing. Our programs are designed to meet the evolving demands of today’s healthcare landscape, equipping graduates with rigorous training
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background in physics and mathematics with a good grounding in geophysics, remote sensing and data analysis. Candidates must have a Master’s degree in geophysics, signal processing or physics, and a PhD in
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plumes, surface temperatures, and calving events by combining ground-truth measurements with remote-sensing data. A new Earth-observing CubeSat mission, DISCO2, will launch in 2025 into a sun-synchronous
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. This PhD project will investigate the interactions between wildfire disturbance and thermokarst dynamics across Siberia and other Arctic regions using multi-sensor satellite remote sensing data provided by
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characterize the spatio-temporal contexts that favor crises. • Development of advanced predictive models (multivariate approaches, machine learning) combining event data, snow and weather data, and remote