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Field
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line at the interface of nutrigenomics, clinical genomics, and toxicogenomics Design and apply bioinformatics workflows and predictive systems biology models Analyse multi-omics datasets from clinical
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to independently drive research projects, analyze and summarize results, publish in scientific journals, and contribute to the supervision of students. Your profile PhD degree in bioinformatics, machine learning
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to therapeutic discovery You bring PhD in computational biology, bioinformatics, computer science, or related field Strong coding skills (Python required; ML frameworks preferred) First-author publication(s
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international research initiatives. This position offers an exceptional opportunity to work at the intersection of genomics, bioinformatics, and translational science. Baylor College of Medicine typically follows
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analysis of multiple disease-specific datasets and contribute to the development of novel methodologies in this space. The ideal applicant will have a strong background in bioinformatics methods and a keen
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of an independent scientific career. Requirements PhD in Neuroscience, Bioinformatics, Computational Biology, or related field Proven track record in relevant techniques for the position applied Proficiency in
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Applications must be in English and include: Motivation letter (up to 1 A4 page), particularly outlining experience in bioinformatics, data science and knowledge of omics data analysis; CV in Europass format
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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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; experience performing metabolic analysis using mass spectrometry, especially in complex matrices; experience with metabolomic pathway bioinformatics analysis and development of rapid point-of-use analytical
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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