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Field
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: Strong background in controlled environment agriculture (e.g., greenhouse or growth chamber research). Experience with experimental design, data collection, and statistical analysis. Experience with sensor
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team also has experience with multi-sensor systems including lidar, inertial sensing within the context of real-time SLAM and autonomous exploration. The researcher will have the opportunity to establish
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of UAV demonstrators at UM6P and OCP sites. Methodology: AI Development: Reinforcement learning, computer vision, and sensor fusion for autonomy. Digital Twin & Edge Computing: Implement real-time
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36 Months, Fixed Term Full-Time Post Position Summary A position has become available for a Post-Doctoral Researcher to work on a new project focused on the use of low-cost sensor networks
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advanced data on nature and biodiversity (derived from AI-supported monitoring and analysis of sources such as satellite imagery, acoustic sensors, and camera traps) can inform spatial planning and decision
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central controller). ● Develop and validate calibration methods for new sensors for turbidity and existing commercial sensors. ● Explore high-frequency turbidity monitoring as a tool for assessing
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for geometry assurance – integrating live sensor geometric sensor data with simulation for real-time control and adaptive assembly. This builds on existing work within the group on digital twins for geometry
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, and iterate the design of a next-generation turbidity meter (Arduino-based with radio communication to a central controller). ● Develop and validate calibration methods for new sensors for turbidity and
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candidate has experience with two or more of the following points. Reactor operation (lab- or pilot-scale) Operation and maintenance of control systems and sensors Proficiency in process modelling and
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sensor geometric sensor data with simulation for real-time control and adaptive assembly. This builds on existing work within the group on digital twins for geometry assurance. AI for automatic tolerance