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Post doc position in theory of machine learning at Department of Computer Science, Aarhus University
A post doc position in theory of machine learning is available. The post doc is under the supervision of Professor Kasper Green Larsen, Aarhus University, Denmark. The focus of the research project
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Job Description We are seeking two outstanding postdoctoral candidates in the field of theoretical catalysis. The postdocs will be a member of the Catalysis Theory Center (CatTheory) at
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. This position is funded by the Danish Research Council Project 1 (DRC-1), which aims to address fundamental problems in geometric singular perturbation theory within the setting of slow-fast analytic vector
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Languages and Theory of Computation (PLTC). Principal supervisor is Professor Philippe Bonnet, Department of Computer Science, bonnet@di.ku.dk Further information on the Department is linked at https
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theory on ‘peripheral synergy’? How does Mobility, and stuckness, configure young livelihoods on the urban periphery, and how may we draw nearer to this through collaborative, multimodal mapping? In what
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Research areas will be within algebraic coding theory. While the primary focus is on fundamental research in algebraic coding theory, practical applications in the maritime industry—including secure
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anthropology contribute to a new general theory on ‘peripheral synergy’? How does Mobility , and stuckness, configure young livelihoods on the urban periphery, and how may we draw nearer to this through
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electronics and proven ability to turn project goals into hardware solutions. You should be able to understand the impedance matching theory and have practical experience with impedance networks for systems
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are defined: 1) Conceptual design principles and methods for resilient manufacturing systems. This topic will build upon existing theory on modular and reconfigurable manufacturing systems and develop methods
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manufacturing systems. This topic will build upon existing theory on modular and reconfigurable manufacturing systems and develop methods and model-based approaches to design and evaluate resilient reconfigurable