10 machine-learning research jobs at University of Southern California in united-States
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Fellowships in health policy, health economics, and health services research with a specific focus on machine learning and data science applications in health economics and outcomes research (HEOR) and
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analysis, expertise in multi-omics data integration, and working experience with computational modeling and machine learning. The ideal candidate will be able to process and analyze high dimensional
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applied to environmental chemical exposures, type 2 diabetes, kidney disease, liver cancer, and renal cancer. The ideal candidate will be open-minded, eager to learn, and enthusiastic about engaging in our
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of genomic, epigenomic, and transcriptomic data processing and analysis, expertise in multi-omics data integration, and working experience with computational modeling and machine learning. The ideal candidate
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integration, and working knowledge of computational modeling and machine learning. The ideal candidate will be able to analyze high dimensional sequencing data, perform network-based analysis (e.g. , WGCNA) and
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of gerontology: https://gero.usc.edu The Irimia Laboratory leverages neuroimaging, neurogenomics, and deep learning to study the aging brain in health and disease, particularly in neurodegenerative conditions like
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processing, imaging, and machine learning to analyze data and help with aggregation, harmonization, and dissemination of our datasets to the research community. More specifically, they will be able
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randomized controlled trial of a mindfulness-based mobile program, a longitudinal survey study examining substance use and mental health outcomes, and a longitudinal study implementing machine learning
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tissue samples. The ideal candidate will have prior experience in the analysis of single-cell transcriptomic datasets and is eager to learn and develop new spatial transcriptomics approach
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in bioinformatics) Background and work knowledge in scientific computing, algorithms, and machine learning or statistics is required Familiarity with R or Python, and the Unix (Linux) environment is