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biopolymer modification for functional films or food products. · Experience in protein hydrolysis and the utilization of hydrolysates as functional ingredients. · Familiarity with bioinformatics-based
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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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construction of proteins that transfer electrons (i.e., oxidoreductases). It is expected that the Postdoctoral Associate will have expertise in several of the following areas including bioinformatics
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genomic data for reconstructing evolutionary patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics
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research in systematic biology. Skills required include bioinformatics, genomics, data science, artificial intelligence, mCT scanning, knowledge of bat biology, and multivariate statistics. Post Doctoral
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includes a diverse team of researchers working on crop breeding, genomics, genetics, curation, and bioinformatics. This is a benefit eligible, non-tenure track position available for two years. The project
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. in Bioinformatics, Computational Biology, Data Science, Artificial Intelligence, or a related field. Proven experience in multi-omics data integration, omics data analysis (genomics, transcriptomics
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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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biology and host–microbe interactions. Quantitative skills in statistics, bioinformatics, or systems-level analysis. Working Environment Standard office and laboratory environment. Optional fieldwork may
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-Sigler Institute for Integrative Genomics and the Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine learning