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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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limited to: Testing underwater frames and instrumentation, including sensor testing and calibration Quantifying benthic biogeochemical fluxes and momentum fluxes in situ from high-density datasets (e.g
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research in digital and high-frequency electronics, embedded systems, control, and communication, utilizing cutting-edge reconfigurable computing and sensors. The aim is to develop next-generation
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including research management, communication, networking, writing of manuscripts etc. Mentoring and career development in a network of national and international collaborators Research exchange to
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adjustment and plate tectonics. The analysis will encompass both network wide and local analysis using data from Greenland GNSS Network (GNET). You will target specific regions where there can be an unresolved
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] that trap light to reduce the number of controllable components. Our long-term goal is to develop a general-purpose PIC platform, so we welcome applicants interested in sensors, optical communications, AI
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grant applications, as well as access to a well-established research network. Moreover, you will have the chance to help build a strong research community and contribute to the development of a high
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of participation in national and international research networks Time spent abroad working at one or more internationally recognised research institutions. Finally, applicants are asked to provide a proposal for
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, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted