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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
, IoT/sensor data, TPTC, LPS, LBS), (d) development of rich VLE prototypes through creation of serious gaming scenarios using state-of-the-art technologies, and (e) validation in large-scale realistic
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Virtual Training Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience
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working with “DTU Smart Road,” a full-scale pavement research platform at DTU’s main campus that hosts embedded strain and temperature sensors. Experiments will also involve the development and installation
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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these. We conduct research within nanophotonics, lasers, quantum photonics, optical sensors, LEDs, photovoltaics, ultra-high speed optical transmission systems, bio-photonics, acoustics, power electronics
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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employees of which around 200 research different aspects of photonics. Research is performed within nanophotonics, photonic nanotechnology, lasers, quantum photonics, optical sensors, LEDs, photovoltaics
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Building Information Modelling (BIM) with Structural Health Monitoring (SHM) applying smart sensor networks, Internet of Things (IoT), and resilience-informed design platforms. Furthermore, REUNATECH
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of Information Backbones including Digital Twins and Building Information Modelling (BIM) with Structural Health Monitoring (SHM) applying smart sensor networks, Internet of Things (IoT), and resilience-informed
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ELISA, immunoassays, electrochemical sensors, and/or mass spectrometry. Solid understanding of biochemical principles relevant to disease biomarkers. Preferable hands-on experience with patient sample