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Field
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: • Image processing and sensor data analysis, ideally with HSI or other imaging technologies. • Machine learning and artificial intelligence, especially classification and segmentation tasks. • High
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field. Research Field: Fluid dynamics, control engineering, or dynamical systems. Required Skills/Qualifications: Experience with experimental methods (laboratory setups, sensors, data acquisition
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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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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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Chemistry, Engineering Chemistry, Biochemistry, Biotechnology, Nanotechnology, or related fields. - Proven experience in one or more of the following areas: o (Bio)chemical sensors o Electrochemistry o
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sensors. We seek a motivated and creative candidate eager to learn new skills, with a strong collaborative mindset and excellent proficiency in written and spoken English. The position offers opportunities
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. Specifically, NIR sensors, hyperspectral imaging coupled with standard or macro lens, spatially resolved spectrometry, evolving plots, and FTIR will be used for the non-invasive characterisation of raw material
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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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, or their LiDAR beam is blocked by a truck. To reach level-4/5 autonomy, we need teamwork: nearby vehicles, drones, and roadside units must co-perceive their environment, sharing and fusing complementary sensor