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Field
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potential impact across quantum networking, communications, and computing. Research Focus Design and fabricate superconducting devices that leverage the nonlinear kinetic inductance of thin superconducting
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networks, from a machine learning and information theory perspective. This basic research project has strong translational potential and aims to elucidate how immune function is altered during sepsis, with
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development Qualifications PhD degree in Bioinformatics, Computational Biology, Computer Science, Mathematics, Physics, or a related field Strong experience with programming in Python, R, or similar languages
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participate in studies that: • Investigate the microRNA regulatory network and other transformation-associated pathways. • Determine how tumor-derived extracellular vesicles regulate T-cell function and immune
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transitions of information flow networks, near critical events. The successful candidate will: Develop and apply Transfer Entropy estimators to high-frequency financial time series. Characterise the temporal
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Postdoctoral Associate Required Qualification: (as evidenced by an attached resume) Doctoral Degree (PhD) or foreign equivalent in Cancer Biology, Molecular and Cellular Biology, Biochemistry, or a
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subsystems. You will work closely with collaborators from Rolls-Royce plc and the OTI. About you You should possess a university PhD degree in mechanical engineering or a similar discipline, preferably with
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SPARK program. The laboratory has strong collaborations across Stanford and beyond, providing opportunities for postdoctoral scholars to expand their scientific expertise and collaborative networks
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-reviewed academic journals and practitioner magazines; Present your research at national and international academic and practitioner conferences and actively develop your (academic and practitioner) network
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and disseminating ethnographic fieldwork an internationally oriented research profile, as documented by a PhD dissertation and/or research publications experience in independently managing research