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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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precipitation, temperature, and soil moisture by leveraging large multi-source datasets from remote sensing, IoT sensors, and climate models. Design and implement deep learning models for forecasting extreme
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participating in the training and management of PhD students. Solid-state spin- photon interfaces are central to emerging quantum technologies, such as optical quantum networks and quantum sensors. For example
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expected to have experience in hydrologic/hydraulic modeling and in one or more of the following topics: in situ sensor installation and flood monitoring, GIS & geospatial big data, AI/ML and data science
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, design and build a LoRa network infrastructure suitable for agricultural environments. Select appropriate hardware and sensors for data collection. Configure and deploy LoRa gateways and end devices
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, biomedical engineering, computer science, or a closely related discipline. Certifications/Licenses Required Knowledge, Skills, and Abilities Experience with motion analysis, wearable sensors, or signal
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data acquisition and analysis. Prior experience with quality-by-design principles, advanced sensor technologies, or continuous manufacturing techniques will be welcomed. An eagerness to identify key
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for information and communication technologies, in particular displays, image sensors and secure communications. The main drive of the group is to carry out applied research which bridges academia and industry, by
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Description The Jones Lab at Georgetown University uses innovative techniques to map greenhouse gas emissions both locally and around the world. The lab is actively engaged in sensor development, field work