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PhD studentship in Machine Learning-Based MIMO Radar Sensing for All-Weather Perception in Autonomous Vehicles Award Summary 100% fees covered, and a minimum tax-free annual living allowance
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, target recognition and shape estimation, data association, as well as intention prediction, beyond the state of the art. In order to support machine learning, the project will make use of historical radar
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Distributed radar systems comprise a coherent network of spatially distributed sensors that can be independently transmitting, receiving, or both. By acting in unison, rather than in isolation
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Almost all radar systems currently transmit from the same location. A drastic departure from this sensing architecture is distributed radar – enacted by a coherent network of spatially distributed
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are poised to re-define our future mobility. However, full autonomy is not possible without all-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms
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to integrate next generation satellite radar (InSAR) monitoring for ground motion with Global Navigation Satellite Systems (GNSS) positioning devices. This will focus on test sites where the student will
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the design and optimization of multistatic and multifrequency radar architectures for near-field 3D imaging. - Contribute to the electromagnetic modeling of radiating systems, wave-object interaction, and
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allocation is of particular interest when a vehicle has multiple ways of being controlled (redundant actuators), where each method has its own advantages and disadvantages regarding factors such as radar
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a related field ▪ Strong knowledge in wireless communication systems, signal processing, or radar systems ▪ Proficiency in at least one programming language (e.g. Python) ▪ Interest in hands
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radar topic and are you interested in a PhD position? Then this position is for you! Job description We invite applications for a fully funded PhD position focusing on advanced geodetic monitoring