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exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep learning models, which find application in image-based science, including in
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-truth data from manual measurements and IoT sensors. The overarching objective of the PDF will be to integrate these datasets to gain new insights into tree growth and response dynamics and trends, and to
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technology, big data analysis, and AI algorithm applications; show innovative thinking for solving complex system control challenges; be capable of interdisciplinary collaboration in microbiology and related
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of sensors, data-acquisition hardware, and environmental monitoring platforms. Demonstrated experience in field-based research — ideally involving outdoor experimental setups, long-duration data collection
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materials, catalytic materials, 2D materials, energy materials, and more. 2) Micro-/Nano-Devices and Integration: Micro-/nano-engines, actuators, multifunctional sensors, energy storage/conversion devices