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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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- 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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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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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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. 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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: 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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in AI: Generative Diffusion & 3D/4D Scene Synthesis: Re-design diffusion and NeRF-style models so multiple agents jointly reconstruct a scene. Semantic-Aware Compression & Network Information Theory