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Field
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Experience Ph.D. in Bioinformatics, Computational Biology, Computer Science, Statistics, Physics, Biomedical Sciences, or a related quantitative field or an M.D./Ph.D. / M.D. with a strong computational
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intelligence, and computational biology. You will be responsible for applying computational and bioinformatics approaches to investigate the mechanistic triggers of VITT and identify strategies to reduce
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statistics, bioinformatics, pathology and nephrology, contributing to open-source methods, publications and translational research. Your key responsibilities will be to: develop and apply computational methods
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conditions by applying omics technologies (e.g., genomics, transcriptomics), validation of gene expression using quantitative reverse transcriptase polymerase chain reaction, and bioinformatic analyses
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sequencing data (e.g., Python, R, or bioinformatics pipelines). Strong record of productivity as evidenced by peer-reviewed publications or preprints. Ability to work both independently and collaboratively in
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mapping and genetic colocalization with GWAS summary statistics. Analyze *-seq datasets using standard and custom bioinformatics pipelines. Contribute to grant writing, manuscript preparation, and
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at research conferences and seminars. Develop statistical and bioinformatic skills through analysis of experimental data and population genetic and genomic data from plant pathogens and beneficial microbes
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experienced bioinformaticians, but have the opportunity to be trained and perform independent in-depth bioinformatic analysis. You will have access to clinical samples from the MitoCamb (https
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or similar bioinformatics workflow management systems. * Experience querying and managing relational databases, including handling SQL queries and database syncing routines. * Familiarity with the biological
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interpret findings. Develop computational and bioinformatics pipelines for spatial transcriptomics, multiomics and imaging data. Integrate tissue imaging, molecular and clinical datasets to generate advanced