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Responsibilities Design and implement digital twins to monitor and optimize urban systems, including infrastructure, mobility, and energy management. Integrate real-time data from sensors and IoT devices to develop
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Responsibilities: Digital Tool Development:Develop digital tools to monitor and analyze the determinants of urban health using sensor data, public databases, and GIS. Predictive Modeling:Design predictive models
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close-to-field conditions, and (ii) a fully autonomous phenotyping robot, Phenomobile.v2+, equipped with a set of sensors (LiDAR, RGB, IR, and Spectrometer) that enable advanced plant measurements
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recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs
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stress. Responsibilities: Monitoring PV panels: temperature, efficiency, dust accumulation. Using environmental sensors and thermal cameras (UAV/IR). Comparative analysis of PV configurations: with vs
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such as air quality monitoring, water leak detection and energy monitoring of electric vehicle batteries. The candidate will need to master sensor technologies, embedded systems, and communication protocols
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project calls. Participate in scientific publication and dissemination of results to policymakers and communities. Required Qualifications: PhD in geomatics, data science, computer science, urban
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infrastructure, mobility, and energy management. Integrate real-time data from sensors and IoT devices to develop dynamic models. Model complex interactions between physical systems (infrastructure) and digital
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set of sensors (LiDAR, RGB, IR, and Spectrometer) that enable advanced plant measurements, that allow us to assess plant’s water budget in response to abiotic stress and nutrients’ application
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of the BMS in lithium-ion batteries, including sensors, actuators, and controllers. In our facility, we are establishing a research unit focused on developing the necessary hardware and software components