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of materials science and engineering. The successful applicant would work with a team of experts including experimental materials scientists, computational materials scientists, machine learning experts
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; DNA sequencing; De novo assembly; Machine learning; Reference materials; Precision medicine; Data science; Artificial intelligence Eligibility citizenship Open to U.S. citizens level Open to
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technology. Reference Lee CH, et al: Exploiting dimensionality and defect mitigation to create tunable microwave dielectrics. Nature 502: 532-536, 2013 key words Electronics; Microelectronics; Machine learning
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. Nature Biotechnology 2019, 37, 555. Genomics; Epigenetics; Transcriptomics; DNA methylation; Bioinformatics; Sequencing; Machine learning; Reference materials; Precision medicine; Data science; Artificial
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, characterize, and optimize interconnects between disparate chip technologies. Applicants will have the opportunity to learn high-demand skills for millimeter-wave technologies including calibration, integrated
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identification of spectral features by computer vision and machine learning. Our computational methods development has three primary goals. The first goal is continued support of expert-driven biomolecular
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catalytic turnover. Integrative modeling and machine learning have the promise of establishing new tools for combining computational and experimental data from HDX-MS and NMR to explain the dynamics and
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We develop and utilize state-of-the-art experimental and computational techniques to acquire, evaluate, and correlate thermodynamic data of standard reference quality with a particular emphasis on
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increase throughput and provide rich datasets that can be exploited by machine learning and artificial intelligence. Current advanced mechanical testing activities involve three-dimensional surface digital
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developing the measurement infrastructure to acquire fundamental property data related to the capture and release of difficult to detect drugs or drug metabolites. We will then design, develop, and