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                are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure 
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                must hold a Master degree in Electrical Engineering (or equivalent), have a solid mathematical background (e.g. in control theory and optimization) and have taken specialized courses in at least one of 
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                increasingly complex networks. By deploying and advancing techniques such as machine learning, graph-based network analysis, and synthetic data generation, the project tackles key challenges in anomaly detection 
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                interdisciplinary research and training program. The objective of the open PhD position is to advance current over-the-air-computing (AirComp) approaches for federated and graph-based Embedded AI to account for 
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                Electrical Engineering (or equivalent), have a solid mathematical background (e.g. in control theory and optimization) and have taken specialized courses in at least one of the following disciplines: advanced 
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                - Experience in image processing and/or - Familiar with rodent behavioural tests and/or - Experience in histology and/or - Familiar with the theory and preferably practical aspect of MRI. You will be responsible 
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                . Expectations for the proposed project We seek candidates whose research intersects meaningfully with the research domains represented across our supervisory team. These include: Narrative theory and narratology 
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                theoretical background for MIMO related research, and have attended courses such as Information Theory, Signal and Systems, Modulation and Detection. Experience with OFDM or single carrier baseband algorithms 
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                : The successful candidate will receive interdisciplinary training in theories and methods for the study of the neuronal correlates of the adaptation to accented speech. This includes many network-wide 
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                systems and control theory. Knowledge of fluid dynamics and related physical modeling. Strong programming skills (e.g. Python, MATLAB, or similar) for data analysis and model development. Ability to work