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-world data, with strong programming proficiency in R or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven
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to develop novel computational methods for data integration and analysis Experience with machine learning approaches for biological data modeling and predictive analytics Good communication skills and
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for analysis of large-scale bulk and single cell data sets Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets Experience with data
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metabolism Strong problem-solving skills and the ability to develop novel computational methods for data integration and analysis Experience with machine learning approaches for biological data modeling and
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, data normalisation and machine learning methods applied to biological datasets Experience with data management and version control (Git/GitHub, workflow automation, documentation) Capacity to work