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Discrete geometric representations such as meshes are a crucial part of engineering simulation pipelines. The success and fidelity of numerical methods heavily depend on the accurate representation
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Master/engineer degree in computer science, applied mathematics, data science with background in image processing, imaging inverse problems, deep learning and optimisation. Good coding skills for numerical
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, or examples, these aspects are of utmost importance and need to be explored to provide convincing and well-grounded arguments [1]. This PhD program will propose to explore advanced methods to detect implicit
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Exploit an existing clinical database containing numerous follow-up FDG/PET exams o Develop modeling methods for the time-series analysis of PET-CT data o Develop a methodological formalism for integrating
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Enthusiasm for applying advanced data science methods including AI-based approaches to environmental challenges and scientific evidence synthesis Technical Skills: Extensive experience in managing and
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the geometrical variability in imaging data. During the project, the candidate will: o Exploit an existing clinical database containing numerous follow-up FDG/PET exams o Develop modeling methods for the time
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Calatroni, and Laure Blanc-F´eraud. Fluctuation-based deconvolution in fluorescence microscopy using plug-and-play denoisers. In Scale Space and Variational Methods in Computer Vision, pages 498–510, Cham
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the exact calculation of the square-root and inverse square-root of the source distribution covariance matrix. This approach offers analytical and computational advantages in comparison to existing methods
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission