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Field
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modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in or experience in environmental exposure
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programming skills in Python - knowledge of mathematics and statistics - very good command of written and spoken English (at least B2) - strong communication and teamwork skills - knowledge
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(e.g., command-line interface, scripting in R/Python, use of common bioinformatics software). Strong foundation in experimental design, advanced statistical analysis, and scientific writing. Proven
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background in 3D perception, including segmentation, localization, mapping and multi-modal sensor fusion; (b) proficiency in programming languages such as Python and C++; (c) demonstrated ability
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interfacial processes in complex materials systems. Advanced data analysis and scientific model development using Python or other scientific programming languages, including experience with automation
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policy, economics, statistics, or a related quantitative field Additional Qualifications Strong skills in Stata, R and/or Python and experience analyzing complex data Demonstrated experience working with
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modelling infectious disease dynamics, population dynamics or fisheries. Proven experience and ability programming in R or Python. Proven experience and interest in the application of mathematical modelling
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. Required competencies: Strong background in bioinformatics (e.g., R, Linux, Python). Experience working with large cohorts and high-dimensional data. Experience with microbiome analysis and/or GWAS
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(preferably in R and/or Python). b) Preferential factors: Familiarity with spatial data analysis and/or Machine Learning methods will be valued. Workplan and objectives to be achieved: The work plan aims
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-based applications (e.g., AWS, Google Cloud, Microsoft Azure), including containerization (Docker), orchestration (Kubernetes), serverless computing, and REST API development. Proficient in Python, with