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Field
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Duke University, Biostatistics & Bioinformatics Position ID: Duke -Biostatistics & Bioinformatics -PD245471 [#29120, 245471 - Halabi] Position Title: Position Type: Postdoctoral Position Location
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field. Strong experience in wet lab techniques, including nucleic acid extraction, sequencing library preparation, and metabolite profiling. Background in bioinformatics for metagenomic and
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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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effective immunotherapeutic targets. Finally, we plan on continuing the path to clinical translation with further preclinical research (proteasome manipulation and optimization studies, bioinformatics
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, or a related field A strong background in chromatin biology, gene regulation, and/ or cardiovascular biology Prior experience with genomics, bioinformatics, or CRISPR technologies What we offer Working
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. The project is conducted in close collaboration with researchers in mathematics, ecology, and bioinformatics. The fellow will be offered affiliation and a workspace at IceLab – the Integrated Science Lab
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are the following: Assists PI with epidemiological and complex bioinformatics data management and analysis in multi-omics microbiome research; conducts research processes and procedures in the PI laboratory. Assists
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are the following: Assists PI with epidemiological and complex bioinformatics data management and analysis in multi-omics microbiome research; conducts research processes and procedures in the PI laboratory. Assists
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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