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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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approaches). Programming experience in C / C + + Python, or similar, and familiarity with scientific computing environments. Ability to work independently and as part of a multidisciplinary
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data. You will have a strong interest in interdisciplinary research in the biological and medical sciences and experience of programming in Python, or demonstrated ability to rapidly acquire fluent
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