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Field
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these machine learning-based proxies together with a postdoctoral researcher working in this project (see below), leveraging data from experiments in our project. Third, you will explore how local connection
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challenges. As a part of your PhD research you will regularly visit our industrial and scientific partners to learn about the challenges and constraints. You will also study the problem in detail with
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during your bachelor, master and PhD courses. For more information about this function, please contact dr. K.E. Konrad, email k.e.konrad@utwente.nl, phone: +31 53 489 3906. About the department You will
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, electrical engineering, technical medicine, or a related field. You have a solid background in biomedical signal analysis, physiology dynamic system, and machine learning technologies, and preferably have
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Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
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Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
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, machine learning, and programming (preferably Python) is highly valued. Effective communication with clinicians and interdisciplinary researchers is crucial, and excellent proficiency in English is required
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wide range of resources and is mostly not publicly available. While sharing proprietary data to train machine learning models is not an option, training models on multiple distributed data sources
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
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Computational Fluid Dynamics (CFD) models; data-based models determined from training/calibration data by system/parameter identification and machine learning. The key challenge is striking a balance between, on