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this Ph.D. topic proposal: • The optimal approximation of 3D shapes using meshes is known to be a NP-hard problem. This means that finding the best possible mesh representation for a given shape, while
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], which states that random neural networks can be pruned to approximate a large class of functions without changing the initial weights. We are also interested in Neural Combinatorial Optimization, where we
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surveillance, random testing, wastewater, hospital surveillance) may help optimize epidemic monitoring, iii) modelling and comparing the patterns of spread of COVID-19, influenza and RSV by age group in France
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. Communication efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, PMLR, 2017. [4] Rieke, N., Hancox, J., Li, W. et al. The future of digital health with
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to the geometric measure theory, Hamiltonian dynamics, calculation of variations, spectral theory, ergodic theory, geometric control (ordinary and partial differential equations), optimal transport. The profile is
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Neural Networks. Nat. Commun. 2015, 6 (1), 6922. https://doi.org/10.1038/ncomms7922. (4) Kida, S. Chapter Six - Memory Reconsolidation Versus Extinction. In Memory Reconsolidation; Alberini, C. M., Ed
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the heart of Europe and associated strategic efforts An excellent research environment combining AI and health at LIH, DFKI and in the entire region A network of interoperable clinical centres across
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familiar with. Indeed, one option is to develop hybrid architectures on the model of PointSIFT [Jiang, 2018] in which the first layers of the network are “freezed” on handmade keypoints, corresponding
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research projects are supported by a network of national and international partnerships, several with public or industrial stakeholders. At the University, the Department cooperates across faculties and with