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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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dissemination of results through presentations and manuscripts. Scholars will work with Dr. Annesa Flentje as a primary mentor during their postdoctoral period, as well as other mentors and staff from the PRIDE
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have the capacity to conceptualize, motivate, design, and implement original research on consequential, policy-relevant topics, and be able to take a leadership role in the research process. The Fellow
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demonstrated interest in improving health and social outcomes through data-driven research. The postdoc will directly work in collaboration with the Principal Investigator Prof. Maria Polyakova and other
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partnership with Principal Investigator Grant Miller (Stanford University School of Medicine) and other research team members, and will contribute to study design, participate in field research, conduct data
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opportunities and time for the scholar to expand their work beyond their primary project. Our team is collaborative, and the principal investigators are experienced mentors committed to supporting the scholar in
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, Flow-FISH, and transcriptomics to profile functional responses of immune pathways in primary patient specimens to diagnose (i) inborn errors of immunity (IEI) and (ii) identify dysregulated immune
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their own research program, under supervision of the principal investigator, as well as work across the many outstanding resources, institutes (e.g. Institute for Human-Centered AI) , and faculty labs across
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, requiring preliminary judgments after the supervisor outlines conceptual approach. Build and organize data as requested by principal investigator or supervisor; use common statistical programs to generate and
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preferred • Experience in image processing and analysis also preferred • The candidate will report directly to the Principal Investigator and will successfully carry out experiments and analyze the data