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Field
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Education Requirements A doctoral degree in epidemiology, statistics, computer science, or a relative quantitative field Experience & Skills Requirements Expert knowledge of R or SAS Additional Qualifications
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% each year and will consist of teaching and other duties. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite
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supervise doctoral students. The position encompasses research and development activities, as well as teaching and curriculum development, in accordance with the job description for a Senior Research Fellow
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analyze large administrative and research datasets. Develop quantitative data analysis pipelines using tools such as Python, Stata, R, or similar statistical programming languages. Assist with occasional
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, Midwifery and Social Work within the Faculty of Biology, Medicine and Health. You will be responsible for: Designing and implementing health economics research drawing on robust statistical approaches in
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project topics of interest – visit: https://orise.orau.gov/eif/default.html Questions? Contact ORISE at [email protected] with the subject line: “EIF Program Question”. Application Deadline 10/2/2026
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University, we create the opportunities you need to achieve your ambitions – because when you thrive, we thrive. We are transforming education for contemporary learners and global citizens. Building on a proud
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, software engineers and bioinformaticians as we develop and leverage technological breakthroughs to explore the biology of complex systems. Principal Responsibilities include 1.) Design and implement reusable
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recruitment) to address the relevant research questions. Perform statistical analysis on survey/experimental data, and Conduct thematic analysis on interview transcripts and open-ended survey responses. To lead
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We are looking for a researcher, whose expertise lies in applied statistical modelling and methods in biological settings, to work with Prof. Gary Mirams and Prof. Simon Preston on an externally