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) Rapid Prototyping (3D printing, Raspberry Pi, FPGA) Electrode patterning and cleanroom fabrication techniques Good team player with strong interpersonal skills Entry level applicants will also be
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maintenance of low-cost DIY (Arduino/Raspberry Pi-based) devices for measuring CO2, CH4, and evapotranspiration (ET). Contribute to fieldwork campaigns, including data collection on rewetted peatlands in
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 18 hours ago
(Arduino, Raspberry Pi, STM32), MATLAB/Simulink, industrial sensors and motor drives, HMIs, and CAD software. Application Materials Applicants should submit: Cover letter describing applied teaching
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platforms, including embedded and edge computing environments (e.g., Jetson, Raspberry Pi, FPGA, neuromorphic chips) Publish results in top-tier journals and present at leading international conferences and
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Raspberry pi cameras to track leaf growth over time in a variety of mutants Stomatal impressions and microscopy to count stomata Image analysis to measure stomatal number, leaf size and hormone concentrations
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(Raspberry Pi and NVIDIA Jetson). Support AI Makerspace users’ robotics projects involving hardware assembly and software frameworks like ROS or ROS2. Maintain and troubleshoot in room hardware including LIDAR
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mentorship of undergraduate students Strong written and verbal technical communication skills Experience working with engineering software and tools (for example, CAD software, Arduino, Raspberry Pi, 3D
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with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer
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qualifications include (but are not limited to): Sophomore or Junior level Software development experience Microcontroller knowledge (i.e. Arduinos, Raspberry Pis, etc.) 3D modeling experience and knowledge of 3D
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and signature matching based on existing code. - Performance evaluation of the application on a Raspberry Pi (RPi). - Development of improvements to machine learning algorithms for anomaly detection and