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Field
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03.06.2021, Wissenschaftliches Personal The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and
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platform. Initially, a black box deep learning approach will be implemented. However, due to the need for robustness, transparency, and explainability (e.g. for quality control across sectors), the research
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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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scholarship is suitable for students with a background in Engineering, Mathematics, and Computer Science. Students with interests in machine learning, deep learning, AI, intelligent decision making
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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling
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Dr Sendy Phang. The student can gain experience and skills in a range of topics, such as Artificial Intelligence and Deep Learning, nanofabrication, computational modelling, metamaterial design, and
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Dr Sendy Phang. The student can gain experience and skills in a range of topics, such as Artificial Intelligence and Deep Learning, nanofabrication, computational modelling, metamaterial design, and
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deep learning and scalable deployment Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run experiments using the latest
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on Artificial Intelligence (AI), Deep Reinforcement Learning (DRL), and Predictive Maintenance for optimizing wind turbine performance and reliability. This research will develop an AI-powered wind turbine