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Be part of the future of creativity support for professionals at Aalborg University! This interdisciplinary project investigates how generative AI can be incorporated into the building design and
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computer scientists to design paradigms that compare "active" learning (standard VR) against "proprioceptive" learning (haptically guided movement), measuring outcomes such as path efficiency, force
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-making, and explainable reinforcement learning for large-scale public-health applications. You will: develop new multi-criteria reinforcement learning algorithms for complex decision problems: you design
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lab researchers in computer science, policy, economics, and related disciplines to design, implement, and analyze state-of-the-art machine learning and research computing approaches. The Machine
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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design, or a related field. We welcome candidates from diverse backgrounds, demonstrating strong collaboration skills and the ability to acquire new skills and technologies. Outstanding spoken and written
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multimodal data. Your responsibilities include: Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing
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artificial intelligence, architectural design, or a related field. We welcome candidates from diverse backgrounds, demonstrating strong collaboration skills and the ability to acquire new skills and
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on practical feedback linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and
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Research and Innovation (UKRI), Entrepreneurship, Art and Science for Environmental Sustainability (EASE) is a radical, 24-month interdisciplinary initiative designed to transform how climate science is