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intelligence, machine learning and data science. You will play an active role in the research team, publish papers, take part in workshops, public events and other activities. The candidates should have a
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children learn new words not only by listening to a storyteller but also by processing multimodal signals such as iconic gestures and gaze direction. Using eye-tracking in both real-life and digital contexts
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teacher education. Using Teach for All as a case-study, the project aims to better understand how and why education polices travel across time and space. While policy mobility is driven by a wide range of
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Infrastructure? No Offer Description The PhD candidate will work on the development of advanced statistical and machine learning methods for time series prediction, with applications mainly in the field of traffic
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such as surgery, patient or staff scheduling using, e.g., multi-objective optimization or machine learning approaches and analyzing efficiency-fairness trade offs. The research will be conducted under
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. These methods will integrate machine learning techniques and real-time sensor data, to enhance operational efficiency, reduce costs, and ensure desired service levels, such as meeting a high percentage of demand
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both independently and as part of an international, interdisciplinary team Assets•Experience with computer vision or deep learning (e.g. PyTorch, TensorFlow)•Familiarity with street view imagery or other
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machine learning You have good programming skills in Matlab, Python You have proven your proficiency in English language equivalent to B2 level You are enthusiastic and result oriented You can work
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Sciences at KU Leuven (e.g. thesis supervision, exam grading, etc.) in Dutch and/or English. You hold a Master’s degree in Law, with a specialization in ICT law. You are eager to learn and fascinated by
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manipulation of fear and reward learning. This position is part of a recently funded, large-scale and innovative research program, aimed at creating new treatments for negative symptoms, among the most