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Landsat and/or Sentinel-1/2 time series. Strong proficiency in Python for geospatial data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very
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management, data analysis and data visualisation of data from clinical studies and registries using Python/R, with methods such as machine learning and network meta-analysis. Eligibility The
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requirements: Master’s degree (MSc) in Bioinformatics, Computational Biology, Computer Science, Biophysics, or a related quantitative field. Strong programming proficiency in Python and familiarity
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professional experience. Furthermore, you have: Several years of experience developing in Python, including type annotations, packaging, dependency management, logging and error handling Extensive experience
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procedures, risk assessments, and local safety regulations Analyse experimental data in R and/or Python and contribute to statistical modelling and visualization Analyse sequencing-based and potentially
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Sentinel-1/2 time series. Strong proficiency in Python for geospatial data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written
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networks, as well as courses in Python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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through literature review and apply that expertise to the analysis of measurements from dozens of different aircraft-mounted instruments. All analysis will be done in Python. The scientific scope
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documentation Work in a BSL-2 laboratory and follow applicable biosafety procedures, risk assessments, and local safety regulations Analyse experimental data in R and/or Python and contribute to
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tools, for example in Python, and contributing to reproducible computational workflows, • compiling, publishing and presenting research results in international journals and conferences and