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Field
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data
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on classical networks, quantum networks are slowly becoming a reality. The coordination algorithms that govern their operation are unlike those employed in classical networks, necessitating novel verification
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positioning, navigation, and timing (PNT) applications for the next generation satellite systems Developing innovative solutions and algorithms to integrate communication and sensing or communications and PNT
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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this data. Clustering travel needs. To define different mobility needs and motivations based on the travel data, we apply different clustering algorithms (e.g., traditional k-means, density-based clustering
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control algorithms lies a physics-based simulation model, whose accuracy largely determines the effectiveness of the control loop. Position 3 – High-fidelity simulation of the LAFP process Current
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., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware optimizations (e.g., automated pipelining). The PhD student will be supervised by Nusa Zidaric. Key
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models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data mining has allowed us to obtain insights from large amounts of data for decades, and it is worth
-
., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware optimizations (e.g., automated pipelining). The PhD student will be supervised by Nusa Zidaric. Key
-
interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data mining has allowed us to obtain insights from large amounts of data for decades, and it is worth