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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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when they are comparable to sensor noise? How can full-field uncertainty be modelled and propagated to support large-scale change analysis? Approach: You will combine advanced robotic localisation
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simulation pipelines for generating annotated RGB-D data 3D semantic perception: Development of learning-based methods for object detection, segmentation, and scene understanding using RGB-D sensor
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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