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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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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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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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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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. Familiarity with frameworks such as TensorFlow and Keras, as well as libraries including Scikit-learn, NumPy, and pandas; - Experience with machine learning models such as Extreme Learning Machine (ELM
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problem-solving skills. Willing to attend and present at in-house and industrial meetings. Proven research experience in quantum machine learning, machine learning, and wireless communications (underwater
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one or more of: computational modeling of social learning, norms, or moral cognition; cultural evolution and gene-culture coevolution; evolutionary game theory and the evolution of cooperation
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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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advanced analytical and statistical techniques to extract actionable insights from complex datasets. Train, evaluate, and continuously refine deep learning and machine learning models, prioritising
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computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability to communicate scientific results clearly through