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environment and governance will be highly desirable. Experience in computational social science, causal inference, text mining, machine learning, analysis of unstructured and multimodal data, or advanced
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study designs, overseeing the timeline of project research outcomes, data collection, management, analysis and interpretation, preparing research reports and research papers, training research assistants
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quantitative research methods. Demonstrate strong research capability and a track record of publications in relevant fields. Have proficiency in data processing and statistical analysis, with experience in
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laboratory protocols. Experience with genomic surveillance and analysis methods will be considered a strong asset. Applicants should demonstrate strong analytical and problem-solving skills, excellent written
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optimizing high-performance C++/C# software modules for real-time control, sensor fusion, and data analysis; developing unity‐based visualization and user-interaction interfaces, including 3D modelling
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proficient in data analysis using statistical or computer modelling software, such as R. They should have an excellent command of written and spoken English and a demonstrated record of publishing academic
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diabetes, obesity, and non-alcoholic fatty liver disease (NAFLD), including experimental design, data analysis, and maintenance of specialized equipment Contribute to grant applications, scientific
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, execution, and regulatory submissions) proficiency in bioinformatics, statistical analysis (R, Python, SAS, or similar) fluent in English (written and spoken) proven grant-writing success and ability
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experiments involving molecular biology techniques and biostatistical analysis Undertake other duties as assigned 5.5 workday per week is required Requirements a Ph.D. degree, preferably with experience in
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analysis, and proficiency in statistical and computer modelling software (e.g. R, Python, Matlab, and C++) would be advantageous. The appointee will work with a research team to study the methodologies