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this project, the doctoral position addresses the evidence layer of the workflow, in which inspection images, 3D laser scans, and sensor data are transformed into condition indicators that form the
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physical sensors A strong background in one or more of the following areas is particularly beneficial: Electrical engineering and signal processing: EMG or other electrophysiological signals, sensor
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from sparse phase-contrast CT acquisitions of oncological specimens Comparison with standard histology and assessment of algorithm performance Present results at international conferences as well as
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) relevant to energy, process, and materials engineering, with a strong focus on: Advanced numerical modelling and simulation Experimental methods and sensor technologies Data analysis of real-world
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an emphasis on object tracking, optical flow and sensor fusion Knowledge of rock mechanics, soil mechanics and/or landslide processes is considered beneficial Prior experience with point cloud
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the AxoMotris part of CEREBRIS, the PhD candidate will focus on identifying movement biomarkers relevant to stroke recovery using clinical and sensor-based data. The position provides exposure to
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software and sensor integration for real-time monitoring of physiological parameters (pH, oxygen tension, glucose, lactate, pressure, flow); and supporting ongoing perfusion experiments in collaboration
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systems design, you will design and build the robots themselves: mechanisms and compliant structures, actuators and tendon routing, embedded electronics and motor control, and the sensor integration