197 parallel-processing-bioinformatics-"Multiple" Postdoctoral positions at Nature Careers
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processing contexts. Current projects span autoimmunity, tumor immunology, and infection. Our group has helped pioneer genome-scale antigen discovery platforms (Cell 2019, Cell 2023, Nature Biotechnology 2024
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the information encoded in our genome to better diagnose, treat, predict and prevent disease. From the individual patient with rare diseases, to the many thousands affected by complex, widespread illness, we
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biological, biochemical as well as -omics and bioinformatics techniques Optimize and apply ribosome profiling and single-cell ribosome profiling on organoids and clinical samples Characterize the impact of gut
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Experience with at least two of the following: flow cytometry, microscopy, NGS techniques and analysis, CRISPR-based epigenetic or genome editing Interest and ideally hands-on experience in bioinformatics
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biodiversity, as well as analyses of past and current land use configurations for multiple facets of biodiversity, i.e. species, functional, phylogenetic, and genetic diversity. The tasks will encompass
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biological, biochemical as well as -omics and bioinformatics techniques Optimize and apply ribosome profiling and single-cell ribosome profiling on organoids and clinical samples Characterize the impact of gut
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Professor Divi Cornec (head of the institute). Candidate profile and skills Applicants must hold a PhD with a solid background and a proven track record in bioinformatics and immunology. They should have
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redox balance. Our studies explore how p53 integrates metabolic cues by acting as both a sensor and regulator of cellular metabolism. In parallel, we are identifying metabolic changes that promote tumor
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intelligence (AI)-assisted image analysis for bioinformatics and medicine. The project is highly interdisciplinary, involving areas of microfluidics, fluidic mechanics, biomedical imaging, and machine learning
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is to use a cutting-edge ensemble of genetic, cell biological, biochemical, organismal, and modern ‘omic’-approaches to achieve a comprehensive understanding of the process of gene expression. CGEN