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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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of programming or scripting languages such as Python, Fortran, R or others. Experience in remote sensing data analysis and geographic information systems. Experience in machine learning motivated algorithms and
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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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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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methods and tools for adaptive, AI-driven embedded algorithms to support real-time analytics and decision-making in IoT, wireless communication, and remote sensing systems Collaborate with multidisciplinary
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proposes to combine in situ and remote sensing data, radiative transfer models, and chemical and physical analytical methods to better understand the fundamental properties and the impact of mineral dust
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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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Global South country (e.g., supply chain resilience, food processing, public health, food safety and security, packaging, agricultural systems). Integrate diverse data streams (sensor data, IoT, remote
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/or remote sensing Personal qualities and qualifications: Self-leadership: Ability to work independently and in a team Curious and bold: Curiosity for other disciplines, open-minded and willing to push
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engineering, computer science, data science, geospatial statistics, or related discipline. Required Knowledge, Skills and Abilities: Familiarity with remotely sensed geospatial data products (UAS and satellite