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., biogeography, evolutionary ecology, genetics). An interest in methodological and statistical development is a plus. Critical thinking, autonomy, and openness are highly valued. Website for additional job details
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genomic diversity and microevolutionary history (Work-Package 3). Integrate heterogeneous datasets (genomic, phenomic, environmental) produced by the project. Perform GWAS to identify genetic loci
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learning, and generative AI Design and implement algorithms for quantum-inspired and quantum-enhanced generative models Investigate theoretical foundations of tensor networks, entanglement, and collapse
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international collaborators. - Production of viruses and infection in an L3 containment laboratory - High-throughput sequencing and bioinformatics analysis - Microscopy (RNA and proteins) - CRISPR genetic
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-throughput technologies (EPIC array, Cut&Run, transcriptomic signatures), functional models (cell lines, primary sample cultures, PDX), genetic editing tools (CRISPR/Cas9), and access to translational research
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5 Sep 2025 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis Research Field Computer science Mathematics » Algorithms
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applied knowledge in evolutionary genetics and epidemiology, particularly on antibioresistance evolution in bacterial or eukaryotic pathogens. Activities : - model the demographic and evolutionary responses
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for: • Contributing to various tasks related to the modeling of lipids and membrane proteins involved in lipid droplet biogenesis. • Developing and implementing the POP-MD algorithm in the OpenMM software
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, complex systems. Strong communication and writing skills; ability to work both independently and as part of a team. About the team The DATA team develops foundational mathematical and algorithmic approaches
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/S0022112006003429 [2] A. Cahuzac, et al. “Smoothing algorithms for mean-flow extraction in large-eddy simulation of complex turbulent flows”, Physics of Fluids 1 December 2010; 22 (12): 125104, doi:10.1063/1.3490063