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Field
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conducting lifetime modeling, developing advanced condition monitoring techniques, and applying data-driven analytics for lifetime prediction. You will play a central role in integrating experimental insights
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to localize anomalous sounds, related to faults, in a complex acoustic environment, characterized by moving sound sources and reverberations. Purely relying on physical models describing the acoustics
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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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/GNC systems has traditionally relied on model-based techniques, which in turn, have their foundations in a solid body of mathematical proofs (such as guarantees of stability and robustness
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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collaboration with modelling (WP3) and societal uptake (WP5) partners; · Development of standardized indicators for evaluating technical, ecological, and societal outcomes. This WP involves close
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so far and to sophisticate them and establishing a platform to achieve international guidelines. The postdoc will work closely with two PhD candidates. One of these candidates investigates
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-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility, Big Data Core and High Throughput Screening Facility. Our research is embedded
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learning Deep learning model generalisation techniques Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object