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Field
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design and evaluate immersive XR experiences that allow users to safely acquire navigation skills in realistic environments such as public transportation hubs, university campuses, and airports. You will
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At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical expertise and transferable skills to tackle tomorrow’s challenges. SIT collaborates with industry in
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in all these approaches. This PhD position aims at developing methods for learning and control in complex large-scale systems. This will be carried out as part of the GreenControl project, whose
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learning and deep learning applied to electroencephalography in the context of brain-computer interfaces, including experience with MATLAB and Python and in the design and conduct of experimental studies
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Solid mathematical foundations and strong programming skills Proficiency in Python and experience with deep learning frameworks such as PyTorch Ability to work independently and take initiative in driving
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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representation learning. Programming skills (e.g., Python) and experience with deep learning frameworks (e.g. PyTorch) Interest in applications to ecological or biological networks Good analytical and
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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty
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the subsequent years, you will combine these insights with predictive modelling and select, train, and validate process-based or deep-learning fire propagation models using a.o. detailed information about
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University. The project is a part of the ambitious Novo Nordisk Foundation Data Science Collaborative Programme, “Synthetic health data: ethical development and deployment via deep learning approaches (SE3D