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community visit: https://greateriowacity.com/build/area-advantages/ The position will begin in Fall 2026, with flexibility in start date based on candidate availability. The initial appointment is for 1 year
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. You are expected to bring deep mechanistic insight, drive the scientific narrative, and leverage our robust pre-clinical models—complemented by our clinical trial multi-omic data—to build your own high
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Contribute to understanding of where and how ERW may be feasible in Aotearoa Work across disciplines and programmes Collaborate with scientists, engineers, and legal researchers within the programme Contribute
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. Requirements PhD Degree in computational biology, bioinformatics, computer science, electrical engineering, or a related field. 3+ years of experience achieving impactful results including publications using
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required for this position. More About This Job Preferred Qualifications: PhD in quantitative field, such as biomedical physics, engineering, computer science, data science, bioinformatics, computational
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candidate is self-motivated and hard-working with a PhD in Data Science, Computational Social Science, Computer Science, or Information Science, with no more than five years post receipt of the PhD
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
Studies (WOS) group at MIT Sloan. Ideal candidates will have recently completed a PhD—or be near completion—with a research agenda relevant to management. Degrees may be in psychology, sociology
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for conferences, grant proposals Data analysis and pipeline development Technology-based solutions for advancing population health To be eligible, candidates must: Have received a PhD in computer
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volunteers Performs other duties as needed Required Qualifications PhD degree in biomedical engineering, electrical engineering, computer science, biostatistics, or a related field Preferred Qualifications
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, the scholar will be responsible for building and maintaining AI-assisted meta-analyses. The scholar will also be responsible for improving generalizable knowledge about the trustworthiness in AI literature