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stochastics group of the Korteweg-de Vries Institute for Mathematics at the University of Amsterdam is inviting applications for a PhD position in mathematical machine learning. The position is part of
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created during synthesis, have previously been developed by the SE department for various targets, including some PFAS. The selected PhD student will advance this work by creating a new electrochemical MIP
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. In this PhD-project, you will propose and evaluate new AI methodology to ensure that organoid data can be used to optimally predict relevant patient outcomes. You will use a real-world case study on
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, employees, IT infrastructure, specialized training). Second, they may require the use of quantitative models, data analysis, and algorithms, but these applications must also safeguard the data privacy and non
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-specific binding sites created during synthesis, have previously been developed by the SE department for various targets, including some PFAS. The selected PhD student will advance this work by creating a
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light on the human brain’s unique vulnerability to vascular disorders. What you do Develop scalable, robust, and reproducible data-analysis pipelines (statistics, mathematical modeling, and ML
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to work both independently and collaboratively in an interdisciplinary, multicultural research environment. Strong analytical skills, with experience in statistical analysis, geospatial data processing
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, partly due to a lack of reliable predictors of transplantation outcomes. The ADORABLE consortium aims to develop an advanced, data-driven assessment system for donor kidneys. Central to this is the use
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for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies
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-analysis pipelines (statistics, mathematical modeling, and ML) for terabyte-scale 3D histology images, from preprocessing to analysis and validation. Handle and visualize large 3D microscopy datasets. Image