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. The lab’s work broadly centers on the development, evaluation, and implementation of technology-enabled interventions aimed at both prevention and management of pregnancy-related conditions and complications
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analysis. The Postdoctoral Associate will work with behaving mice and develop experimental and analytical approaches to understand circuit-level mechanisms of cognition. Among the key duties of this position
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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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developing ADRD studies of deeply phenotyped participants at high ADRD. Bloods are collected longitudinally and assessed for different types of omics. The postdoc will work closely with the PI and a team of
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, molecular and large omics data studies. In addition to experimental research and contributions to maintaining the laboratory, the Postdoctoral Associate will be expected to prepare manuscripts for publication
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in conjunction with a unique tip-enhanced spectroscopy instrument, currently under development in Prof. Rabitz's lab, to produce the world's first nanoscale dual-frequency comb laser spectrometer
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directly or indirectly support DDSS-supported projects or its mission. Candidates will be able to apply and further develop their technical skills in a dynamic research environment. The successful candidate
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molecular biology techniques. Basic knowledge of statistical analysis and interpretation of biological data. First author of at least one scientific publication indexed in the ISI/Web of Science database
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the Computing and AI Division at Los Alamos National Laboratory is seeking highly motivated Postdoctoral Research Associates to drive the research and development of novel sparse neural networks. This position is
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a unique opportunity to develop cutting-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency