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(morphological patterns), based on the experts’ knowledge. Then, tools like Procrustes analysis, linear dimensionality reduction (PCA) and standard clustering algorithms are employed. A first objective of our
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the use of synthetic data in precision medicine research and applications through development of AI algorithms, tools and other processes to allow for the enrichment of clinical data sets Providing training
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of the surgical procedure (direct filming and arthroscopic video feed), and a device for recording heart rate. In the second phase, the student will propose signal processing and data fusion algorithms to reconcile and
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aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimising PIC algorithms for modern
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the ability of neural networks to learn unknown posterior distributions distributions. Their use in the field of image microscopy, however, remains limited. The purpose of this PhD thesis is to develop
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the use of synthetic data in precision medicine research and applications through development of AI algorithms, tools and other processes to allow for the enrichment of clinical data sets Providing training
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,..) machine learning techniques and their application to engineering problems is also crucial A solid background in high-performance computing and algorithm design is highly valued Experience with hybrid
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Virtual laboratory to predict the ability of a fluctuating biomass to satisfy a material use-VARIOUS
be taught within the various courses at the ECN and NU with a 50/50 distribution between the engineering (ECN and NU Polytech) and Master’s 2 courses. Concerning the ECN, it would be desirable
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a vast body of data and detailed characterization of the associated memory circuits and their dynamics. This vast body of literature has led to the notion that memories depend on distributed neuronal
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, 2020, Proceedings, Part XIV 16. Springer, 2020, pp. 194–210. [8] C. Reading, A. Harakeh, J. Chae, and S. L. Waslander, “Categorical depth distribution network for monocular 3d object detection,” in