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Field
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collaborative and social abilities. Prior experience with metabolomics, transcriptomics, bioinformatics, and imaging is a plus. For more information, please feel free to contact Per Svenningsen at psvenningsen
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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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. 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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patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics of squamate reptiles; the largest group
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blot, histology, immunohistochemistry), Cardiac cell and tissue culture (incubator), Bioinformatics (transcriptomics, metabolomics, proteomics), Signal processing (optical action potentials, electrograms
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with bioinformatics tools and analyses. • Experience with electrophysiology and proficiency in MATLAB/Python/R models of auditory and vestibular diseases. FLSA Exempt Full Time/Part Time Full Time Number
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background in computational biology, machine learning, and bioinformatics, with a passion for understanding the underlying mechanisms of gene regulation. The position provides an exciting opportunity to work
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in genome-wide computational experiments. We are part of the Center for Medical Genomics and the Center for Computational Biology and Bioinformatic s. We have strong links to Penn State College
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral