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guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental
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. Expertise in one or more of the following areas would be an advantage: Life cycle assessment, Life cycle social assessment, Techno-economic modelling, Materials flow analysis, Energy storage or battery supply
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, multilevel and multidimensional measure of structural racism. The ideal candidate should possess a strong background in measurement theory and statistics, demonstrating mastery in structural equation modeling
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is a partner for the Alrek Health Cluster, Årstadveien, Bergen, and is supported by the Trond Mohn Foundation related to Complex Conditions and Ageing (CC.AGE). Read more about the project: https
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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of innovative computational procedures and methodologies, addressing a national skills shortage and enabling timely progress on a high-impact research initiative in modern econometric modelling. The role provides
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, and research team to ensure timely achievement of project deliverables. Undertake the following specific responsibilities in the project: i. Develop, train, and optimise deep learning models for object
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capability Ability to communicate complex information clearly Fluency in relevant models, techniques or methods and ability to contribute to developing new ones Ability to assess resource requirements and use
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quality modelling approaches to analyse the flow interactions between sewer discharge of surface runoff, as well as the dynamics of pollutants and pathogen propagations associated with water movements. The
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vegetables. Model bacterial pathogenic levels through complex survival and growth patterns. Utilize either data analysis or molecular analysis tools (or both) to inform strategies to optimize pre-harvest