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Field
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and mathematical modeling (Python, MATLAB, or R) Design of controlled experimental systems to test mechanistic hypotheses Application Instructions Interested candidates should submit the following
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including in programming languages such as R and Python. Excellent publication record in peer-reviewed journals or conferences Experience of working in multidisciplinary projects including interaction with
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such as STATA or R. Demonstrated experience in writing research proposals and commissioned research tenders. Demonstrated ability to work independently to meet competing deadlines. Ability to undertake a
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Skills: Strong analytical skills and demonstrated experience with statistical programing in R. Experience using geospatial data for landscape-scale research. Experience with Species Distribution Modelling
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, bioinformatics or related discipline Strong computational skills, with expertise in scripting in BASH and either R or Python Experience with analysing complex datasets on high-performance compute clusters A track
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their ability to contribute to one (or more) of the following cross-disciplinary research initiatives: Scale computing and data analytics to exascale and beyond for science and energy Deliver transformational R&D
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, emphasis will be placed on: Experience with relevant R&D work (publishing, methodological knowledge) in the field. Experience in organization and management in healthcare services. Themes and perspectives
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diverse talent. Reach out to talent@uq.edu.au for accessibility support or adjustments. Applications close 9 July 2025 at 11.00pm AEST (R-51743). Please note that interviews have been tentatively scheduled
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doctoral qualification (or equivalent experience) in a relevant discipline Strong quantitative analysis skills (R, Stata, or SPSS), with a track record of research outputs Experience in health services
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). Minimum of two years related experience. • Demonstrated skills in data management and programming using statistical packages (SAS and SQL and occasionally STATA and R); • Demonstrated knowledge