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for intelligent brain-computer interfaces? We are offering a PhD position in analog/mixed-signal CMOS circuit design for EEG and wearable sensor interfaces, as part of a pioneering project focused on assistive
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measurement techniques/ sensors. Experience with system modelling and simulation (e.g., TRNSYS, Python, or similar tools). System and control engineering (e.g. digital twins, model predictive control) –pre
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estimation, and implementing sensor-based feedback control strategies. The project will also explore AI-based and reinforcement learning (RL)-based control approaches to enable intelligent and adaptive robotic
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for soft and continuum robots, integrating advanced sensing technologies for shape and force estimation, and implementing sensor-based feedback control strategies. The project will also explore AI-based and
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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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DTU Tenure Track Researcher in Low-Noise Supercontinuum Lasers and Supercontinuum Laser based Opt...
for the position. DTU Electro has more than 300 employees with competencies in electrical and photonics engineering. Research is performed within nanophotonics, lasers, quantum photonics, optical sensors, LEDs
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development of implements for managing main and support crops in the field, tested on stationary gantry robots and mobile platforms. Work includes lightweight, structurally optimized mechanical design, sensor
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. Technology for people DTU Electro has more than 300 employees with competencies in electrical and photonics engineering. Research is performed within nanophotonics, lasers, quantum photonics, optical sensors
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on the following: Combining passive and active cooling strategies. To optimize sensor types, the number of sensors, and locations withing cooling system and building to facilitate efficient monitoring and fault
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materials/ Latent thermal energy storage is an advantage. Hands-on experience with experimental setups and measurement techniques/ sensors. Experience with system modelling and simulation (e.g., TRNSYS