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Field
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. Optimizes the performance and scalability of AI/ML workloads through algorithmic and system-level improvements, including evaluation and tuning of CPU vs. GPU usage for cost-effectiveness. Monitors and
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researchers from oncology, radiobiology, computer science, genetics, statistics, and basic/translational clinical research. Our research couples state-of-the-art computational and experimental techniques
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Scheduled Hours 40 Position Summary Performs innovative data analyses on neuroimaging and related data. Develop and implement computer algorithms for analyzing that data. Assume major responsibility
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Health Access Services This position will be responsible for performing essential tasks required for the creation and revision of Epic scheduling decision trees (algorithms) and for leading other Maestro
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& Responsibilities: Designs, develops, and implements: Algorithms and computer software for omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical.] Data management and analysis
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instructors posting on the university careers website. Application materials should be addressed to the faculty noted on the job description or to the chair of the program that you are applying to in cases
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, transcriptome sequencing, methylation assays, metabolomics, and/or lipidomics assays. State-of-the-art computational algorithms and new methods will be applied and developed to prioritize the variants and genes
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machine learning techniques, predictive algorithms, and AI-powered tools to extract actionable insights to drive US Commercial strategies and tactics. Manage and mentor a team of data scientists (internal
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), to start a research career in an environment committed to training outstanding cancer research scientists of the future. The Institute's particular strengths are in genomics, computational biology and
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Description Primary Duties & Responsibilities: Implements: Algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management