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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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(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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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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will benefit from a first-class environment at CEA LIST with access to a large number of reference tools and a strong experience in design and analysis of secure systems, in particular against fault
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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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techniques capable of handling: Extremely large design spaces with many interacting variables Multi-objective trade-offs (performance, power, area, sustainability, etc.) Complex constraints and architectural
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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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, 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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, 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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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