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, epidemiologists, clinicians and lab researchers, with expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis
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that thrive with imperfect data, creating adaptive models that can quickly learn from new machines with minimal training data, and integrating these predictions with optimization algorithms to make cost
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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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expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis. The supervisory team will consist of dr. Maarten
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
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sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and
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quantitative modeling; Strong expertise in programming, including proficiency in languages commonly used in data analysis and machine learning, such as Python; Excellent verbal and written communication skills
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
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predictive optimization, behavioral modeling and machine learning. There is vivid interaction within the group to foster collaboration both with scientific and social activities. The PhD candidate will also
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Intelligence (AI) and machine learning (ML) techniques. You will develop AI-based predictive models to anticipate user engagement, primarily using data collected through unobtrusive measurements (e.g., websites