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, sensor failures, or the aggregation of datasets from multiple sources. There is a rich literature on how to impute missing values, for example, considering the EM algorithm [Dempster et al., 1977], low
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only to healthy knee bone structures in the context of elective orthopedic surgery [5, 6]. Moreover, many of these models are developed by private companies for implant placement, limiting accessibility
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slicing. - Develop advanced AI/ML algorithms and data analytics techniques to automate and optimise exposure requests, adapted to available resources and real-time demand. - Propose and
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algorithms will be developed to extract discriminative and predictive features from a multimodal dataset consisting of digital histopathological images, lung CT images, clinical, genomics, and multiproteomics
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generally to develop new distributed edge services. The successful candidate is expected to evaluate metrics such as latency, throughput, availability, and resilience in different applications scenarios
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molecular targets critical for developing new therapies for rare diseases, based on genetic data and biological system simulations. -Computational Drug Repurposing: Developing novel algorithms and databases
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modeling the dynamic of the data evolution is clearly important. The purpose of this postdoc position, within the Institut 3IA Côte d'Azur (Univ. Côte d’Azur & INRIA), will be focused on the development and
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of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we
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. Côte d’Azur & INRIA), will be focused on the development and the understanding of deep latent variables models for unsupervised learning with massive heterogenous data. Although deep learning methods and
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analyzed. The tensor model structure estimated by suitable optimization algorithms, such as that recently developed in [GOU20], will be considered as a starting point. • Exploiting data multimodality and