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(large scale heterogenous data synthesis, meta-analytic studies, conceptual synthesis) Experiences and interests in shaping modern team science research and interest in super-visioning & coordinating
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group: MARIANNE (https://team.inria.fr/marianne/). The MARIANNE project-team pursues high-impact research in Artificial Intelligence with a focus on data and models for computational argumentation in
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the Research Facilitator team and the FSTM's financial controllers to provide consistent, strategic project support Further information: Please contact the team leader of the Research Facilitators team, Your
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the analysis of large-scale health data, to systematically integrate evidence and identify patterns across diverse health outcomes. The ideal candidate will bring a proven interdisciplinary background
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within a coherent computational model is currently challenging, due to the typical large dimension and complexity of biomedical data, and the relative low sample size available in typical clinical studies
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, ranging from biological to clinical features. The integration of such heterogeneous information within a coherent computational model is currently challenging, due to the typical large dimension and
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, IRCAN, ISA). His/her group will leverage large-scale, high-dimensional datasets—such as genomics, transcriptomics, proteomics, imaging, or single-cell data—to uncover fundamental biological mechanisms. We
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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applications, where we have to deal with detailed and large-scale datasets, often coming from a variety of sources ranging from traditional CAD modelling to 3D scanning. The aim of this research position is to
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model without sharing their personal data; FL reduces data collection costs and protects clients' data privacy. In doing so it makes possible to train models on large datasets that would otherwise have