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Field
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algorithm development and satellite remote sensing • Good written and spoken English • Ability to work independently as well as in a team • Proficiency in programming languages (e.g. Python, R, Fortran
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Eligibility criteria . PhD in atmospheric sciences • Knowledge of cloud physics • Experience in algorithm development and satellite remote sensing • Good written and spoken English • Ability to work
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-based models, and remote sensing technologies. Required Qualifications • Ph.D. in Civil or Environmental Engineering, Hydrology, Data Science, Geosciences, Computer Science, or a related field. PhD must
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possible renewals, an accumulated period of 3 (three) years in this type of scholarship, consecutive or interpolated. Proven knowledge in: Data Science (Python) Machine Learning (Python) Remote Sensing
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detection (vision, force, voice), contribute to real-time control software and remote monitoring interface development, and oversee data collection during clinical user trials. They will work closely with a
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detection (vision, force, voice), contribute to real-time control software and remote monitoring interface development, and oversee data collection during clinical user trials. They will work closely with a
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-frequency sensing and data transfer (ISAC) are integrated into the same devices. The research project is part of a larger consortium, gathering world-class researchers in remote sensing with expertise ranging
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and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated ability to conduct independent research and publish high-quality
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and pasture phenotyping, focusing on the use of advanced remote sensing technologies as drones and satellite imagery. The fellow will develop phenotyping tools and protocols to identify traits
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Associate to undertake fundamental and applied research into novel deep learning approaches to analysing LiDAR and vision data in forest imaging and remote sensing. The role is part of a three-year project