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parameters. The model is used for condition monitoring and fault detection using methods focusing on statistical methods using residual generation and Kalman filtering. Qualifications: Phd and master's degree
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to the continuous development of the Gaussian-Linear Hidden Markov Model ( GLHMM ) toolbox. In this role, you will be responsible for applying and validating statistical testing methodologies using datasets obtained
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of survey research, from planning and ethics approval, through pilot work and data collection, to statistical data analysis and write-up. Qualitative research skills and experience are an advantage but not
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beyond. Hence, we seek a proactive candidate with a strong background in statistics and economics. Your primary tasks will be to: Design workflows for quantitative societal sustainability assessment of low
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statistics) 8. Experience with clinical research and understanding of research methodology 9. Experience with data analysis 10. Good interpersonal skills to develop and maintain effective working
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genetics, animal breeding, statistics, or a related field. Proficiency in statistical analysis of genetic data related to insect breeding and experience in developing insect breeding strategy. Experience
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methane emission traits in dairy cattle. The candidate must have Ph.D. degree in quantitative genetics, animal breeding, statistics, or a related field. Proficiency in statistical analysis of genetic data
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statistics either from their studies or previous jobs, as well as knowledge of statistical programs such as Stata, SAS, R or similar. Your efforts will contribute to exciting research and give you the
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of survey research, from planning and ethics approval, through pilot work and data collection, to statistical data analysis and write-up. Qualitative research skills and experience are an advantage but not
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science, statistical analysis, and mathematics. Experience with programming languages such as MATLAB and Python. Excellent analytical, problem-solving, and modeling skills. Strong written and verbal