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Field
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processing influence the resulting quantitative data. What you’ll do Generate systematic proteomic datasets as part of a collaborative team effort Develop and apply statistical and quantitative approaches
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perturbations, including changes in gene expression, enzyme activities, cellular phenotypes, and metabolite production; and 3) performing statistical analyses of human clinical and/or omics data to evaluate human
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completed degree are met before the (intended) start date of the post-doctoral education. The ideal candidate will have a strong background in bioinformatics, statistics, and computational biology, with
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biological domains is a must PhD in computational biology, computer engineering, computer science, (bio)statistics, artificial intelligence, physics, or related. Desire to push the frontier
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fertility using bovine and mouse models. Analyze and integrate genomic, transcriptomic, and epigenomic datasets using computational and statistical approaches. Develop bioinformatic workflows and contribute
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strong scientific background with relevant expertise in cell and/or molecular biology. Interest in programming, computational biology and statistic towards high-throughput data analysis is considered a
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established and emerging bioinformatics, statistical modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics
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field assessments; experience in dataset organization, statistical analysis, and/or soil health assessment frameworks; previous publication(s) of scientific paper(s) on soil macrofauna; familiarity with
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, or sonar sampling of the benthos ● statistical fluency (e.g., mixed effects models, Bayesian statistics) applied to noisy field biomonitoring data or long-term ecological datasets ● experience operating and
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from vaccination, natural infection, and controlled human infection studies. The Postdoctoral Research Scientist in Computational Biology will apply established and emerging bioinformatics, statistical