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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at Master’s level in Electrical Engineering, Computer Science, or Applied Mathematics, with a minimum of
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(e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects of machine learning such as
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distributed computational pipelines and optimizing communication costs. You will also contribute to the integration and testing of the models in real D-MIMO environments, in close collaboration with a PhD
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existing and creating new deep learning-based models for anomaly detection, theoretical and numerical studies of detection quality, creating new distributed computational pipelines and optimizing
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such as satellites, LEO constellations, fixed links, automotive radar, and Earth observation stations, using techniques from signal processing, communications theory, optimization, and AI. You will join the
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part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at Master’s level in applied mathematics
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inference and deployment costs (e.g., model compression/simplification and hardware-aware optimization). We are also interested in how resource-efficiency interacts with broader sustainability aspects
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advanced courses in Computer Science, Electrical Engineering, or Applied Mathematics. Alternatively, you have gained essentially corresponding knowledge in another way. The requirement for a degree must be
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. The employment requires strong subject knowledge in optimization, mathematical modeling, and quantitative analysis. You are a problem solver who works well with complex issues, understands complicated written