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HIL environment rather than only on offline simulation. Learning outcomes anticipated include stronger understanding through immediate feedback on live systems, deeper engagement with
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experimental studies with healthy volunteers using psychophysical methods, psychophysiological measurements, mathematical modelling, and machine learning approaches. RESPONSIBILITIES: Participate in
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Information Management, or related fields; Have basic knowledge of machine learning models in supervised and unsupervised learning tasks (i.e., k-nearest neighbours, Decision Trees, Neural Networks, Logistic
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of ecosystem degradation and the aforementioned extreme climatic phenomena; (v) explore a “Machine Learning” analysis to explore the importance of other environmental and managerial factors in the spatial and
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points); b) Publications in the area of artificial intelligence, machine learning, computational simulation and multi-agent systems (maximum 5 points); c) Research experience in the project area (maximum
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the time you start work. You have knowledge in finite element modelling or machine learning for vision. Hands-on experience with image acquisitions and different types of cameras (visible, infrared) is a