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advanced statistical machine learning, reinforcement learning, and gen-AI-driven decision models for supply chain and operations optimization. • Design scalable algorithms for demand forecasting, risk
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should operate in an AI context. The AI revolution has sparked ongoing debates that highlight the multifaceted role of AI and algorithms in shaping our world—in ways that engage deeply with law. Fully
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on Education and AI (NOLAI). You will create methods to predict energy consumption, create energy labels for algorithm scalability, and guide implementers in choosing more efficient algorithms. Ready to make AI
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of solvers for stochastic optimization problems, and test the methods on real-life data. As part of the PhD you will be following advanced courses to extend your skills, implement and test algorithms, and
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approach could resolve this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat
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contributions to standardisation efforts. Your role This fully funded PhD position focuses on exploring and developing hybrid quantum-classical algorithms to tackle sustainability challenges. Key areas include
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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Post doc position in theory of machine learning at Department of Computer Science, Aarhus University
is on understanding and improving the performance of classic learning algorithms, in particular Boosting and Bagging, both in terms of speed and generalization capabilities. The project also allows