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Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
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(Dairy and Ecology: co-innovating business operations for nature), primarily funded by the Dutch Research Council (NWO). The project embraces four fully-funded PhD candidates and a postdoc. It is a
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machine learning. You will develop and evaluate AI-driven visual speech recognition models and contribute to their integration into a smart-glasses prototype. The system aims to convert non-vocalized lip
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addition to doctoral students, bridge fellowships for outstanding Master’s graduates and exploratory research grants for postdocs are regularly offered. All three groups of researchers form the interdisciplinary
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or AI/machine learning/NeuroAI. For candidates with a neuroimaging background, this may include experience with data acquisition, preprocessing, and/or analysis; experience with fMRI is preferred, but
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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of tax data. Core goals include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools
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scientists (Master student, PhD student, postdoc, senior scientist), currently of six different nations, which are inspired by working together on solving technical challenges to advance fundamental questions
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning