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: Alexandre José Malheiro Bernardino (ist13761) Organic Unit: Scientific Area of Systems, Decision and Control Scholarship Theme: Computational Auditory System Simulators and Machine Learning-based Optimisation
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software for aerospace precision machining — you will develop physics-informed machine learning models that learn how individual machines actually behave, and use those models to drive a genuinely
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on novel applications of machine learning techniques on mobility data towards resilient, safe, inclusive and sustainable urban micro-mobility systems. You will become member of an international, 38-partner
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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collected from these studies, subject recruitment, and some administrative work. Depending on qualifications/interest, the research specialist may also assist with developing computational models of learning
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Science in an urban landscape. The teachers will use a project-based experiential learning model, utilizing the outdoors to facilitate learning. This model uses co-teaching - 2 teachers total with one in
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to the stage of contracting the scholarship, and before that, they may be replaced by a declaration of honor. Preferential factors: • Expertise in Machine Learning; • Experience in developing machine learning
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technologies, such as low-power long-range (LoRa) and high-throughput, low-latency technologies (5G). In the context of machine learning, communications play a central role in data sharing and in the decision
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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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mitochondrial quality leads to neurodegenerative diseases and impairs neuronal recovery. To approach these questions we use a combination of genetics, biochemistry, cell biology and imaging in a number of models