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interpretability and predictive accuracy. We want to explore the structural parameters in choice models (e.g., random coefficient logit models) and obtain valid inferential results using machine learning-enhanced
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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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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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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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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
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unpleasant in hand vibrations. An in hand discomfort model can be developed using machine learning approaches. Finally, we build an “in hand vibrations” discomfort standard. Organisation The University
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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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modeling and statistical machine learning. We put equal emphasis on developing universally adaptive theories and sensory-specific methods. Through system-level developments, the theoretic-based contributions