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, motivation for applying for the position, and a link to previously produced code on GitHub or equivalent. If previous code cannot be disclosed, provide a motivation. Bachelor/Master theses work/other relevant
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or plant phenotyping, or image analysis. Experience with isotope tracing, physiological measurements, or nutrient analysis. Skills in statistical modelling (e.g. R), multivariate analysis, or trait-based
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imaging, mathematical modelling, and functional genomics, receiving experimentally testable predictions generated by state-of-the-art predictive models. These predictions will be rigorously validated using
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algorithms for Bayesian machine learning with applications in e.g., medical image analysis. The doctoral student position is offered within the machine learning project “The Challenges for Machine Learning in
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registry data is a plus). Familiarity with biomedical ontologies, controlled vocabularies, or coding systems such as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), NPU (Nomenclature
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constraints such as sensitive devices running in a medical environment could be considered. Keywords for this project: code analysis, static analysis, reverse engineering, defense mechanisms, vulnerability
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harmful content or incorrect code. This project introduces a new idea: instead of relying on opaque, heavy machine-learning models, it learns transparent, rule-based rewriting systems that can transform