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, Enviromental, and Geomatic Engineering, has an opening for a PhD student. This position focuses on leveraging vehicle sensors, remote sensing, and machine learning to support modern urban road safety analysis as
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to their environment. On the biohybrid side, the lab has recently demonstrated 3D-bioprinted muscle-tendon interfaces with enhanced force transmission (Science Advances, 2025) and sensor-embedded muscle enabling closed
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development of new sensors, support nanoparticle-based cellular reprogramming strategies and identify new omics-based biomarkers. We work closely with clinical partners and we focus on deep understanding
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expand both actuation and sensing capacities with light. The project will create an artificial sensorimotor network with densely distributed actuators and sensors similar to the biological neuromasculature
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machine perfusion platforms for human and rat liver This will include learning the engineering design, assembly, and operation of the perfusion hardware; developing improved control software and sensor
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the chemistry of superheavy elements – the newest and heaviest members of the periodic table. Through innovative single-atom experiments, state-of-the-art instrumentation, and international collaborations
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these complexities by utilizing high-resolution data (e.g., high-frequency measurements from on-board vehicle sensors and computer-vision imagery) to provide near real-time insights. However, it is not yet clear how
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expertise and interest in machine learning and computer vision algorithms is necessary, with an emphasis on object tracking, optical flow and sensor fusion Knowledge of rock mechanics, soil mechanics and/or
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analysing time-series data from smartphone sensors and usage logs using advanced statistical and machine learning approaches to develop personalised nudging and adaptive interventions. The candidate will also
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human participants Acquire and manage multimodal physiological data during wake and sleep such as pupillometry, EEG, ECG, respiration, photoplethysmography, actigraphy, wearable sensor data, smartphone