FELLOW, POST DOCTORAL - Civil, Architectural and Environmental Engineering 00086999
Missouri University of Science & Technology
Rolla, MO
Full-time
Engineering
Posted on April 26, 2024
Hiring Department
Civil, Architectural and Environmental Engineering
Application Deadline
Open Until Filled
Full Time/Part Time
Full Time, Benefit Eligible
Job Description
Post doc is sought in the area of data-driven plant science and stress assessment to support remote sensing in environmental engineering, starting in November 2023 or shortly after. This position focuses on understanding the interactions between plants and biotic and abiotic stresses using drone hyperspectral remote sensing and with satellite data. We are broadly interested in addressing questions related to the detection of physiological and biochemical responses of plants to the impacts of various environmental stress from various species and at landscape scales. Our approach integrates various methods and tools including but not limited to remote sensing, spatial science, biological modelling, and machine learning.
Candidates must have a PhD and experience in remote sensing (especially hyperspectral remote sensing), plant ecophysiology, computer science, agriculture, environmental engineering or science, or related disciplines. A strong background in image processing (especially from hyperspectral sensing), computer science, machine learning, and/or engineering will be an advantage. The successful candidate is expected to work closely with graduate students in environmental and civil engineering and coordinate with technical staff members at the Center for Intelligent Infrastructure (CII) – one of nine university research centers at Missouri University of Science and Technology. The CII staff members are specialized in big data and information technology, civil and environmental engineering, computer science and engineering, and mechatronics and mechanical engineering. Artificial Intelligence and Machine Learning. The successful candidate will help develop a deep learning detection approach for plant stress responses in drone-based thermal, LiDAR, and hyperspectral imagery. In addition to the infrared and hyperspectral cameras, a Global Positioning System (GPS)/Inertial Measurement Unit (IMU) device will be mounted on a drone for assessing plant stress related to environmental conditions and toxicant exposure. In the data process task, an online user-friendly platform integrated with deep learning models will be developed for automatic image analysis and localization.
Qualifications
PhD and experience in remote sensing (especially hyperspectral remote sensing), plant ecophysiology, computer science, agriculture, environmental engineering or science, or related disciplines.
Preferred Qualifications
A strong background in image processing (especially from hyperspectral sensing), computer science, machine learning, and/or engineering will be an advantage.
Other Information
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