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Field
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-Design Methodology We are seeking a highly motivated PhD candidate to join the TACIT project, focusing on accessibility, disability studies, (co-)design methods, and Human-Computer Interaction (HCI
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explore how artificial intelligence can be used to predict the response to therapy in cases of acute rejection of the kidney graft. Using machine-learning techniques, you will assess kidney transplant
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based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored, and the findings of your project will be
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machine learning to improve outcome prediction and patient stratification. deepen our understanding of the etiology, clustering, and diagnostics of cardiovascular diseases in critically ill patients
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top journals. Visibility at relevant international academic conferences and experience in teaching or thesis supervision (especially in data science / machine learning) are highly appreciated. Our offer
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in the fields of control and systems theory, cyber-physical systems, optimization, artificial intelligence, machine learning, and systems engineering.
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. The research will combine computational modeling (e.g., NLP, machine learning, deep learning) with human-centered research (e.g., user studies, experimental design, qualitative analysis). We are looking not only
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insights for best practices and design guidelines. Collaborate across the TACIT project: Collaborate with other PhD candidates and researchers working on the project to share insights and learn from its
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rich theoretical and computational environment supported by the Multimodal Language Department. Requirements Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP
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, materials science, or computer science with an interest in complex materials for computing and learning. Excellent candidates with training in any area of science or engineering will be considered. PhD