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- LIST - Luxembourg Institute of Science and Technology
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for cyber-physical systems (CPS), Internet of Things (IoT) devices, autonomous systems, and other emerging smart technologies. We protect smart systems by developing novel techniques for usable authentication
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within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer
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, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction
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for industrial applications (e.g. building integration PV) and novel self-powered photovoltaic-based devices for Internet of things (IoT) applications (e.g. sensors, wearables, printed electronics). The group
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includes security, IoT, unmanned aerial vehicles, integrated satellite-terrestrial networks, quantum communications, spectrum management, tactile internet, earth observation, and autonomous transportation
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for autonomous robotics, underwater IoT, mobile healthcare and smart farming. Eligibility The PhD Researcher must be an ‘early-stage researcher’, i.e., at the time of recruitment have not already been awarded a
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computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics
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sensing, IoT sensors, and climate models. Design and implement deep learning models for forecasting extreme weather events such as floods, droughts, and heatwaves, integrating probabilistic approaches
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departments in Europe. We focus on a wide range of aspects of computer architecture – from edge and IoT devices to HPC and cloud sys-tems, from AI accelerators to quantum computing systems – as
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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