528 parallel-processing-bioinformatics positions at Yale University in Ireland-University-Ranking-2024
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genetics, immunology, computational biology, systems biology, neuroscience, bioinformatics, or a related field Strong background in either wet-lab experimental biology or computational/statistical genetics
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approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory grammar
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, retinal diseases, and vision science. Training opportunities may include imaging analysis, bioinformatics, statistical analysis, and grant and manuscript development. The specific training plan will be
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human genetics and/or statistics, including knowledge of genomewide methods, such as GWAS and post-GWAS analysis, and comfort with bioinformatics. This is highly collaborative work, so the candidate
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these questions and advance novel therapeutics for infertility. The candidate will use bioinformatic and computational tools to analyze single-cell and spatial transcriptomic data. Your work will be central to
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through peer-reviewed publications. Qualifications Candidates should possess: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genetics, Molecular Biology, or a related discipline. Strong
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conferences, and through peer-reviewed publications. Qualifications Candidates should possess: Ph.D., completed or expected, in Bioinformatics, Computer Science, Statistics, Computational Biology, Biomedical
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the ability to perform bioinformatics/systems biology analysis. The role will include bench work including tissue culture and tissue processing, performance of molecular assays, and data analysis
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, Consumer Health Informatics, Translational Bioinformatics, Human-Computer Interaction, and Data Science, with applications in electronic health records, digital health, biomedical imaging, healthcare policy
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facilities and vast resources to support professional development. Responsibilities Develop bioinformatics pipelines for the analysis of large genomic datasets Derivation of novel statistical and theoretical