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strategies that are robust with respect to perturbations and capable of balancing multiple competing performance criteria. Information Uncertainties are inherent in many engineering design problems and can be
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machine learning challenges: how to learn from limited and heterogeneous data, how to combine data with physics-based models, how to solve inverse problems under uncertainty, and how to build models
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imaging, and inverse problems. Your goal will not simply be to implement algorithms, but to turn cutting-edge imaging research into reliable, accessible and extensible software – and to develop new
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imaging (crucial) Experience with image segmentation, deep learning, or computer vision. Experience with 3D image processing or inverse problems. Experience with experimental research and data acquisition
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to design detector-integrated photonic crystals for this endeavour. Job description We are seeking a highly motivated PhD candidate to join the INTERPRETER project, an interdisciplinary research initiative
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or more of the following topics: numerical methods for (partial) differential equations. optimization or inverse problems. data assimilation or uncertainty quantification. agentic and generative AI. solid