25 algorithm-sensor-"CNR" PhD positions at Technical University of Denmark in Denmark
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Ghaddar (DTU/Ivey/IE) and Professor Claudia D'Ambrosio (LIX Ecole Polytechnique/CNRS). You will be enrolled at DTU and you will be part of a vibrant international network with research visits at LIX Ecole
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agricultural robotics and new sustainable farming practices. The PhD projects will be combining new sensor systems and perception algorithms. So, if you are one of the 2 selected applicants, your primary
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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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—such as microtubes—while also performing embedded functions like weight and color detection. The project will combine multi-material 3D printing, sensor integration, and adaptive control, aiming to push
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will learn and adapt the realms of the combustion modes and fine tune the performance for each while the engine is operated. Self-tuning, adaptive, control algorithms will be used. This part of the three
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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal
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for sensing the backscattered ultrasound signals. You will join a laboratory with expertise in photonics, piezoelectric materials and device fabrication methods for sensors and actuators, working under
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
the furthermore, core works are envisioned in (c) initiating novel VLE design using foundational Digital Twin for Construction Safey (DTCS) components (e.g., city models, nD BIM, IoT/sensor data, TPTC, LPS, LBS
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, Responsibilities and qualifications Electricity markets are undergoing a rapid transformation: Market participants are deploying AI algorithms towards making their bidding decisions. AI algorithms are instructed
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale