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Field
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learning algorithms into professional software with an intuitive user interface, incorporating feedback from CHWs through iterative design and evaluation cycles. The selected candidate will be part of a
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. The Liu Laboratory focuses on the integrative modeling of transcription and signaling pathway activation. The lab develops algorithms for the analysis of gene expression arrays and next-generation
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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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algorithm sensing, navigation and control of robot systems, including mobile robots and robot arms/manipulators; designing and implementing algorithms for machine learning, computer vision, and/or estimation
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sensor networks, robotics, energy, and Smart Cities • Network systems and economics, cooperative control and optimization, game theory • Distributed algorithms for decision making • Machine learning and
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well as in generative techniques for multi-modal images. The incumbent will contribute to novel algorithm development, detailed performance characterization and analysis, and trade studies involving
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advanced sensing and controls algorithms for manufacturing Communicate research results through presentations, reports, conference papers, and peer-reviewed journals Deliver ORNL’s mission by aligning
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Scientific Machine Learning. The successful candidate will develop and deploy state-of-the-art SciML algorithms in high-performance computational physics codes. We accept applications from all candidates with
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research involving biological data analysis and modeling of biological systems. In particular, they will develop and apply algorithms to construct discrete dynamic models of signal transduction networks
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data, metabolomics and/or proteomics. Develops robust pipelines for data annotation, analysis, and quality control. Creates analytical algorithms and tools to address scientific questions with big data