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Where to apply Website https://www.academictransfer.com/en/340637/phd-machine-learning-meets-choice-mo… Contact City Groningen Website http://www.rug.nl/ Street Broerstraat 5 Postal Code 9712 CP STATUS
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well on various excellence ranking lists. FEBRI, the graduate school and research institute of the Faculty of Economics and Business has one PhD position in the field of Machine learning meets choice
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PhD Machine learning meets choice models (1.0 FTE) (V24.0232) « Back to the overview Job description Since its foundation in 1614, the University of Groningen has enjoyed an international reputation
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PhD Neural Control of Vocal Pitch: Exploring the Intersection between Speech and Language Processing
expertise in speech motor control and vocal pitch processing, and Dr. Frank Tsiwah, who brings expertise in neural processing of language and machine learning techniques for linguistics research. Since its
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for a highly motivated and qualified PhD student/researcher who will work towards developing new (machine learning and deep learning-based) algorithms and procedures that exploit human labelling and
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PhD Neural Control of Vocal Pitch: Exploring the Intersection between Speech and Language Processing
Dr. Frank Tsiwah, who brings expertise in neural processing of language and machine learning techniques for linguistics research. Since its foundation in 1614, the University of Groningen has
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quality of traditional HTR algorithms. We are looking for a highly motivated and qualified PhD student/researcher who will work towards developing new (machine learning and deep learning-based) algorithms
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new (machine learning and deep learning-based) algorithms and procedures that exploit human labelling and machine-based clustering efficiently. Human labelling (from Dutch cultural heritage partners
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mobile robotics. Towards agile sensing and inference, a series of original studies will be conducted based on uncertainty-aware kino-dynamic modeling and statistical machine learning. We put equal emphasis
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uncomfortable or unpleasant. The results from human perception will then be used in the model to predict unpleasant in hand vibrations. An in hand discomfort model can be developed using machine learning