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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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micronutrient analysis of tree crops. Performs statistical analysis of metabolite and micronutrients. Establishes laboratory protocols for metabolite analysis. Leads and coauthors the writing of research-based
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
molecular biology in a collaborative, multidisciplinary environment. Minimum Education and Experience Requirements PhD, MD or equivalent degree in related field. Required Qualifications, Competencies, and
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. Qualifications Basic qualifications (required at time of application) PhD in Political Science, Economics or a related Social Science OR be enrolled in a PhD program at the time of application. Additional
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). Qualifications Minimum Qualifications: PhD in Nutrition, Biochemistry, Physiology, or related field. Degree conferred by the expected start date. Ability to work independently and as an interdisciplinary team
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neuroscience community at the University of Oslo and Oslo University Hospital. The group includes postdoctoral researchers, PhD students, a laboratory technician, a laboratory manager, and a dedicated data
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Qualifications PhD in neuroscience, computer science, biostatistics, psychology, cognitive science, or a related field. Strong experience in neuroimaging data analysis and interpretation. Strong experience with
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technologies, data science, and research software engineering, a. This position offers the opportunity to work in a world-renowned research institute alongside faculty, software engineers, PhD students, and
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to diverse audiences Help oversee the activities of the Science Communications Team Required Qualifications: PhD in the social sciences with emphases on communications, education, diffusion of innovations
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell