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biostatistics, bioinformatics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and statistics / machine learning is required Previous work and
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complex neurological and cancer disorders. Your profile The candidate will have an MSc or equivalent degree in biostatistics, bioinformatics, computational biology, machine learning, or related subject
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candidate, with a strong background in the development of machine learning methods for bioinformatics. The project focuses on the development of new neural network architectures to perform inference
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department and the Plant Reproductive Strategies (SRP) team. Our team focuses on the evolution of plant reproductive systems, using diverse approaches including theory, experimentation, bioinformatics, and
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researcher will use molecular resources and develop bioinformatics tools to describe the taxonomic diversity of pathogen communities. In addition, using metabarcoding approaches based on environmental DNA
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the southern area of Grand Paris. We offer an international, highly collaborative research environment with access to state-of-the-art core facilities, including advanced imaging, genomics, and bioinformatics
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, clinical and histological data in a translational framework. Main activities: - Bioinformatic analysis of WES and RNA-seq data. - Somatic variant detection and annotation. - Statistical and clinical
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targets. Main activities: - Bioinformatic analysis of WES and RNA-seq data. - Variant calling, annotation, and mutational signature analysis. - Differential gene expression and pathway analyses
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biology, bioinformatics, computer science, biology, or a related discipline, Strong interest in applying quantitative approaches to complex biological and biomedical questions Solid computational skills