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combines: Fluid dynamics and heat transfer (theory and experiments), Computational modeling, and Machine learning / computer vision for data analysis and pattern recognition. The goal is to improve
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and small, contribute to a better world. We look forward to receiving your application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with
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research in collaboration with stakeholders. The PhD project is part of two key initiatives: the Competitive Timber Structures research profile and the BioGlue Center: Competence Centre for Bio-based
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reinforcement learning for edge-cloud-based computation. In both cases, vehicles and robots must be able to navigate around obstacles and manage safety constraints along the planned trajectory, even in situations
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of computation, and thus continuous aspects, into rule-based models of graph transformation in order to combine the individual strengths of both paradigms. Rule-based models are transparent and explainable
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Are you curious about how AI can transform learning in public environments? This is an opportunity to explore conversational AI and visualization in an interdisciplinary project with strong research
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corresponding knowledge in another way. Experience in one or more of the following areas is considered meritorious, Self-supervised learning, image denoising, or inverse problems, Transformer-based architectures
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administrative duties, up to a maximum of 20% of full-time. The working language is primarily English, but you are expected to acquire basic knowledge of Swedish during the employment period. More information
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thus continuous aspects, into rule-based models of graph transformation in order to combine the individual strengths of both paradigms. Rule-based models are transparent and explainable; they make sense
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is on analysing first-person descriptions of conscious experiences with the help of machine learning and large language models (LLMs) to identify, compare, and systematize different types of states of