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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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research work will be to devise efficient algorithms for source separation in DAS measurements. Issues such as large data volumes that can exceed 1 To per day and per fiber, instrument noise, complex nature
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through text, images, networks, … A similar situation can be encountered in the context of medical data, where the data types may be even more large. It is therefore of strong interest to be able to analyze
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to constitute a hospital-university institute (IHU) from 2025. This Institut reConnect will tackle the topic of big data in audio-vestibular patients and the work performed by the engineer will form a solid basis
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of machine learning models to integrate structural data, binding geometries, and cell surface topology. Mapping and prediction of epitope combinations enabling optimal immune synapse formation for ICE and
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to perceive their environment because this sensor can produce precise depth measurement at a high density. LiDARs measurements are generally sparse, mainly geometric and lacks semantic information. Therefore
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Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities
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data that mimics key characteristics observed in experimental recordings. This approach will involve an inference phase to identify large-scale parameters characteristic of larva populations. Crucially
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the main bioinformatics software and methods (R, Python, Bash). Knowledge on large-scale genotyping/sequencing data analyses. Good level in statistics. Good level of written and oral English. Ease in a