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architecture, design, and construction. Design portfolio, presenting 3-5 examples of architectural or other projects showing your skills in circular/sustainable design, computational design and digital
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urban planning. Research environment This PhD position is part of the Sustainable Urban Water and Environmental Engineering (SUWEE) research area within the Department of Architecture and Civil
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on developing novel memory and logic devices based on TMDs. These will target both near-term industrial applications and emerging neuromorphic (brain-inspired) computing architectures for AI accelerator hardware
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PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
, you’ll also join WASP —Sweden’s largest individual research program ever, offering unparalleled resources, networking and career opportunities. Be part of shaping the future of AI! Project overview
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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technology, building physics, HVAC systems, computer science and control systems architecture, thereby advancing all disciplines involved. The project is a collaboration with Building Services Engineering
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will be part of a dynamic and inspiring working environment in the beautiful city of Gothenburg on the West coast of Sweden. About us The Department of Computer Science and Engineering is a fully
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student for a project at the intersection of mathematics and deep learning to work on theoretical aspects of geometric deep learning. The question whether more data and compute are sufficient to improve
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We invite applications for several PhD positions in experimental quantum computing with superconducting circuits. You will work in the stimulating research environment of the Wallenberg Centre
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technologies for medical diagnostics, treatment, and monitoring. Our research activities span computational modeling, algorithm development (using both traditional signal processing and machine learning