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This PhD explores light estimation and synthetic relighting of real scenes by combining generative deep learning with computer graphics. It aims to reduce issues such as hallucinations and temporal
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Do you want to be part of a young, dynamic research group working on designing the next generation of sustainable energy materials using computational chemistry and machine learning? And do you see
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infrastructure. The research will investigate how machine learning models can be designed and deployed efficiently on constrained hardware platforms while supporting the reliability and security requirements
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testing and condition monitoring using modern machine learning, including multimodal foundation models and related data-driven and physics-informed approaches. Research topics may include visual and real
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combines multimodal data sources, physical models, and advanced machine learning to create new forecasting and communication tools. The lab is looking for candidates for the following two stipends: Stipend 1
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Disentanglement of the Exceptional Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and
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quantitative analysis of register, survey, and patent data to various qualitative methods. If you have an interest in, or experience with, novel computational methods such as NLP, machine learning, and AI
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candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a master’s degree in human-computer interaction, computer science, interaction design, applied
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the project based on your interests and in collaboration with a leading architectural firm. The candidate is expected to publish in leading Human-Computer Interaction venues. Your competencies You hold a PhD
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Engineering, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) • Strong programming skills (e.g., Python) • Strong skills in machine learning, deep learning and modern