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d'apprentissage automatique (machine learning, deep learning). - Une aisance dans l'embarqué sur arduino, raspberry pi est requise. - L'envie d'apprendre de comprendre et d'approfondir au-delà de la réalisation
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deployment of IoT solutions for environmental and energy applications. Solid knowledge of embedded electronics and microcontrollers (Arduino, ESP32, Raspberry Pi, etc.). Expertise in developing sensors for air
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(e.g., KiCad, Eagle, Altium, or similar) Embedded systems or development boards (Arduino-class, ESP32, Particle, Raspberry Pi, or similar) Low-voltage wiring, connectors, and harnesses in instruments
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systems, microcontrollers, and DAQ hardware (e.g., Raspberry Pi, Arduino, National Instruments, DeweSoft, CANbus data loggers) for real-time sensor data collection Experience developing or deploying machine
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for the measurement of chamber-based gas fluxes, spectral properties, and/or environmental parameters, through integration of hardware, sensors, electronics, microcomputers (e.g. Raspberry Pi, Arduino, ESP32 or similar
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development. Experience with DSP/FPGA/Microcontroller/Microprocessor (such as Raspberry Pi) platforms for controller development. Network and develop collaborative R&D with other groups and divisions internally
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-person, based in Pittsburgh, PA. Successful applicants will have experience in micro-electronics system (Arduino, Raspberry Pi, etc.) design, programming and usage and/or experience in network design and
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logging device to pair with Raspberry Pi data collection systems to monitor the soil, air, and humidity in five high tunnel structures throughout Valley and Adams County. • Assist at tabling and outreach
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equipment - with minimal supervision (e.g., Python, Arduino/Raspberry Pi) - Experience designing and building equipment or devices (laboratory instrumentation preferred) - Hands
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systems (e.g., ESP32) and/or microcomputer systems (e.g., Raspberry Pi), with skills in hardware, including digital circuit design and PCB layout; (c) have hands-on experience in one or more of the