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resourceful, organised, and able to manage multiple priorities effectively. e) Be highly numerate, analytical, and be willing to work with complex datasets. The ideal applicant should also: a) Have experience
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• Proficiency in at least one statistical software (e.g., R, Stata, SPSS, Python) • Expertise in quantitative analysis, with preferred skills in o Quasi-experimental evaluation techniques (e.g., Difference-in
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within the team under the Principal Investigator Assistant Professor Borame Dickens alongside multiple collaborators and experts. Methods include agent based/individual based modelling, SEIR modelling
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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and data analysis tools commonly used in bioinformatics (e.g., shell scripting, Python, R). Solid understanding of omics data, including metagenomics, RNA-seq, and metabolomics. Experience with
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or adaptive designs. • Proficiency in at least one of the following: R, Python, JavaScript, or other relevant data analysis or app development tools. • Familiarity with EMA, EMI, MRTs, or SMART designs is