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Requisition Id 14394 Overview: We are seeking a High-Temperature Materials Processing Researcher. This position resides in the Surface Engineering and Tribology Group in the Materials Structure and Processing Section, Materials Science and Technology Division (MSTD), Physical Science...
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and/or R knowledge will be given priority. This individual will act as the lead team member for a specific administrative data set and will assist other lead members on their assigned administrative
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and/or R knowledge will be given priority. This individual will act as the lead team member for a specific administrative data set and will assist other lead members on their assigned administrative
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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-scale job ads datasets, spatial datasets, patents). Conduct data analysis using econometric and statistical tools. Excellent knowledge of R is expected. Good knowledge of Python, experience with modern
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economic studies funded by the UK National Institute for Health and Care Research. Experience of conducting economic evaluations using suitable statistical software (e.g. STATA, R or SAS) is essential
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, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted in the Department og Microbiome Dynamics of Prof. Gianni Panagiotou of the Leibniz-HKI in
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proximity extension assay (PEA) Assistance with sample handling during autopsies Compilation, visualization, and correlation analysis of data, as well as statistical analyses using R Qualifications
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a bachelor of pharmacy degree and a Ph.D. degree in pharmacy will be preferred. They should have a strong quantitative and qualitative background; experience in analysing epidemiological data using R
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area, have extensive experience in conducting model-based economic evaluations using suitable statistical software (e.g. R or Python) and the ability to work independently, prioritise your own workload