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JOB PURPOSE: In support of Stanford University’s over 9,000 PhD students and postdoctoral scholars, Stanford Career Education (CareerEd) seeks a talented and committed professional to serve as the
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both qualitative and quantitative research methods to develop comprehensive insights into the determinants of health and well-being in older adults. Collaborative Research: Actively collaborate within a
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QUALIFICATIONS: PhD or Master's degree in a relevant field such as data science, bioinformatics, computational biology, or statistics Strong programming skills, including experience with R or Python Experience
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prestigious scientific outlets. Advanced oral, written, and quantitative research skills, exhibiting fluency in their area of specialization. Experience working with public agencies, communities, and URM groups
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projects in the energy domain. Evidence of research productivity and quality via publications in top journals or other prestigious scientific outlets. Advanced oral, written, and quantitative research skills
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applicant will take a lead role in development and application of models and contribute to a broader portfolio of projects through regular participation in working groups. Candidates must have a PhD in Health
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interdisciplinary collaborative statistics unit in the Biomedical Informatics Research Division within the Department of Medicine at Stanford University. The mission of Stanford's Quantitative Sciences Unit is to
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professional development to prepare them to succeed in a PhD graduate program in physics or astronomy. Selected interns will be expected to relocate to the area and find housing. Program Goals: Research
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participate in the design, evaluation, and validation of new performance-based assessments of foundational learning skills. They will also receive training in, and will apply, quantitative research methods used
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data, and disseminate findings. Independently work with investigators on proposal or grant development. Take the statistical lead on studies. Oversee biostatisticians in the lower levels on data cleaning