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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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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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, 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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, which performs numerical analytics during the simulation. This is necessary due to the ever-growing gap between file system bandwidth and compute capacities. To this end, we are developing the Deisa
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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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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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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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The Biostatistician – Clinical Trials will join the Methodology group within the LIH’s Competence Center for Methodology and Statistics (CCMS). Within the Department of Medical Informatics, the CCMS
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at all levels; offer vital services to scientists in the public and private sectors within the member states; develop new instruments and methods; and engage actively in technology transfer