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aerial vehicle (UAV) imagery collection and processing, deep learning methods, and rangeland vegetation communities in Oregon and Idaho as part of an interdisciplinary team including researchers in plant
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/ROS 2, Linux, Git, and robotic software development. • Hands-on experience integrating robotic software with UAV hardware, cameras, embedded computing platforms (e.g., NVIDIA Jetson), and
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particular emphasis on integrating satellite LiDAR and UAV data with field observations. Applying statistical modelling, automated machine learning approaches, and artificial intelligence for the analysis and
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through field-based surveys, and from ground-based sensors, proximal sensors and UAVs and satellites. These data are often used to drive process models, that ultimately are used to optimise aspects
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expected to work in investigating the use of data and imagery to monitor the coastal environment. Other responsibilities can include: • Lead the design, planning, and implementation of UAV-based remote
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particular emphasis on LiDAR-Inertial Odometry (LIO), Visual-Inertial Odometry (VIO), and multi-sensor fusion for UAVs and other agile platforms. The Research Fellow will develop high-performance, robust
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operational conditions. Ability to work across AI, robotics, control, and UAV autonomy domains. Ability to deliver research outcomes within project timelines and contribute to high-quality publications We
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or ecological monitoring). A track record of peer-reviewed publications relevant to remote sensing, ecosystem modelling, or carbon assessment. Working with LiDAR-derived forest structure metrics. With UAV-based
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on a broad range of topics, including advanced physical layer design, multiple-antenna systems, reconfigurable intelligent surfaces, integrated sensing and communications, non-terrestrial and UAV
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communications, non-terrestrial and UAV networks, physical layer security, quantum-enabled communications, semantic and AI-native wireless systems, and energy-efficient/low-power communication. Our vision is to