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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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radiochemistry, computational chemistry, and/or nuclear materials science. Preferred Qualifications Depending on the project, experience with Python, machine learning techniques, and any of the following tools is
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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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(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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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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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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photonic design Proficiency in Ansys Lumerical, Tidy3D, Python, MATLAB, and GDSfactory Strong written and oral communication skills Ability to work independently under strict timelines with excellent
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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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development, including Python, model integration, and system architecture design. Excellent communication and leadership skills, with prior experience in mentoring students or leading small teams. PhD degree in