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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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for recent MD and PhD graduates who are passionate about leveraging computational methods to transform trauma and acute care surgery. Fellows will work at the intersection of clinical medicine, data
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statistical and computational methods motivated by potential applications to observational studies. The work will involve both methodological research with biostatistics faculty and collaboration with clinical
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must be sent to sinbelangero@gmail.com , with a copy to pgtunifesp@gmail.com , with the subject line: “Application for PD Fellowship - BHRC - Name + Surname.” Requirements: PhD in health or biological
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the dynamic of OM in the future global climate change by using Soil Organic Models. Mentor master students and PhDs Education, qualifications, and experience Applicants must have an earned doctorate in Agronomy
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statistical data for analytical purposes and judging the validity of the data). Job Duties Preparation of research manuscripts (original manuscripts with research data or reviews) and research reports related
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. Requirements PhD degree or above preferably in Computer Science, Data Science, Statistics, Information Systems, or a related field. Minimum of 5 years’ experience in data engineering, data ops, or AI/ML data
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, analyze, and solve a variety of problems. Ability to exercise sound judgment in making critical decisions. Department Specific Qualifications Education: PhD in Psychology, Computer Science, or related
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assist us with the review process. You may upload these documents to the application under CV/Resume. Required Education and Experience Appropriate PhD in a related field Preferred Qualifications PhD in
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(Investigating how climate change may increase risk of zoonotic spillover by changing human and wildlife movement, stress, and behavior). This position requires a PhD in ecology, disease ecology, epidemiology