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Field
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
present at international conferences. Mentor graduate students and contribute to building capacity in AI and health data science at UM6P. Required Qualifications: Ph.D. in Bioinformatics, Computational
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-motivated postdoctoral researcher with a strong background in biology and experience in bioinformatics. Experience in multiplex proteomics and sequencing analysis and a track record of effective communication
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a non-federal source. The position involves applying bioinformatic and statistical tools to existing multidimensional datasets, including metagenomic, transcriptomic, proteomic, and metabolomic
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approaches including targeted genetic murine models, primary cell culture and analysis, multi-omics and bioinformatics. The biological focus will be on vascular biology, immune cell function and metabolism
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. in Basic Science, Health Science, or a related field. No experience required. Preferred Qualifications A Ph.D. in Genetics, Bioinformatics, Computational Biology, or a related field. Strong background
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early development of the placenta, and a basic understanding or willingness to learn bioinformatics is desired. The candidate should be proficient in, or highly motivated to learn complex 3D culture
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responsibilities include bioinformatics analyses with a strong focus on single-cell RNA sequencing and next-generation single-cell barcode lineage tracing, as well as the interpretation of complex biological
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral
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-motivated postdoctoral researcher with a strong background in biology and experience in bioinformatics. Experience in multiplex proteomics and sequencing analysis and a track record of effective communication
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neurodevelopmental contexts. Conduct rigorous data analysis using bioinformatics tools and frameworks to manage, analyze, and interpret transcriptomic data at multiple scales. Collaborate within a large consortium