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Field
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) Documented record of advanced quantitative methods skills in R and Python, specifically Experience with GIS and spatial data analysis Experience with natural language processing or text-as-data approaches
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showing the marks of all subjects studied. Specific Requirements Familiarity with statistical programming languages (e.g., R / RStudio and/or Python) and statistical methods will be valued. Familiarity in
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prosthetics. The project will be supervised by Prof. Sarah Cartmell, Prof. Julian Yates, and Dr. Jose R. Aguilar Cosme at the University of Manchester. While prosthetic materials continue to evolve, current
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bring strong programming skills with languages like Fortran, C, R or Python. Prior experience in the field of process-based modeling, e.g., climate modeling, is an asset. For both projects, fluent
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public datasets as well as samples generated through collaborations. Ideal candidates will have strong interests in microbiome-host biology, bioinformatics, or machine learning. Experience with R or Python
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and scalable pipelines (e.g., using Snakemake, Nextflow, or custom scripts). Automate common data processing workflows in bash, R, or Python. Maintain version control using GitHub and contribute
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analysis and research approaches related to the sustainability and equity implications of innovation and/or industrial policy in Africa. - Experience with Research and Development (R&D) and/or innovation
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, preferably in neuroscience, psychology, or arts and health, including the creative arts therapies. Preferred: demonstrated expertise in data analysis and statistics (e.g., R, Python, MATLAB, SPSS), preferably
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, initiative, and the capacity to work effectively in a team are essential. Prior experience with econometric software (e.g., Stata, R) and field surveys in developing countries will be considered an asset
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 15 days ago
Code of Conduct for the Recruitment of Researchers, i3S adopts the Open, Transparent and Merit-based (OTM-R) principles for the recruitment of researchers, with the aim of conducting fair and transparent