210 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" PhD scholarships in Netherlands
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the European or Caribbean territories (the public entities Bonaire, St. Eustatius and Saba and the autonomous countries Aruba, Curaçao and St. Maarten) of the Kingdom of the Netherlands for more than 12 months
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well as the HERITOUR consortium. Job requirements To be eligible, a candidate must have: Not resided or carried out their main activity in the European or Caribbean territories (the public entities Bonaire, St
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of Technology Mathias Peirlinck creates 'digital twins' of the human heart: personalised computer models that map the functioning of each unique human heart. This work paves the way for the development of more
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brings together expertise on transition management and Science and Technology Studies (STS). The main goal is to further co-develop with the field hands-on methods to support the responsible, societally
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organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with
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modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning). Experience in annotation software such as ELAN and PRAAT. Existing peer-reviewed journal publications and conference
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You will join the “Professional Learning & Technology” (PLT) section of the Faculty of Behavioural, Management & Social Sciences. The PLT section specializes in research on professional learning in and
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and understanding of complex biological systems and biodiversity. You will get the opportunity to learn about both simple and complex biological models, computer programming, data visualisation, and
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description This project addresses the effective design of a military supply logistics network, composed of transportation and communication links such as roads and rail, aerial drone routes, and nodes, such as
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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover