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Field
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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score derivation and validation, and other relevant analyses. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility and efficiency
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record of peer-reviewed publications. A background is required in computer programming (including Julia and/or C/C++), applied mathematics and statistics. Please upload your application materials via
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, including fitting and manipulation of large array-type data sets (using Python, Matlab or equivalent) Ability to communicate well, and work within a collaborative team environment Preferred Knowledge, Skills
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Proteomic and metabolomics analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job Description Primary Duties
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Sequencing (GBS), Whole Genome Sequencing (WGS) and targeted genotyping chips (e.g. MassArray) and analyze them together with environmental data using population genetics and machine learning tools
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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific