83 computational-physics-"https:"-"https:"-"https:"-"https:"-"L2CM" positions at Aalborg University
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. CLASSIQUE is organized into four research thrusts that rely upon interdisciplinary competences in: communication theory, networking, information theory, physics, mathematics, computer science, and statistics
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system design. Hardware security or cyber-physical system security concepts. Experience with embedded platforms, FPGA development, or hardware-accelerated computing will be considered an advantage
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of Computer Science, the Technical Faculty of IT & Design. We invite applications for two fully funded PhD stipends in the area of Natural Language Processing (NLP), Knowledge Graphs (KGs), and Large Language Models
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-Anthropology programme at Aalborg University. Your competencies Applicants should have a strong interest in human–robot interaction and collaboration and in the role of emerging technologies in healthcare
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collaboration with the Department of Computer Science at Aalborg University, combining socio-technical research on human-robot collaboration with technical research on interaction technologies and robotic systems
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areas: cyber security privacy engineering cryptography and applied cryptography computer engineering edge or cloud computing and networking. You will be part of one of the department’s research groups in
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emphasizes adaptive and resource-aware design under uncertainty in networked communication systems. The first theme deals with Digital Twins for Physical AI over Wireless Networks. The researcher will study
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of complex systems. Experience with high-fidelity numerical modelling, for example using computational fluid dynamics or advanced process simulation tools, is highly relevant. It is an advantage if you are
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the Department’s psychology programme, including course development, examination, and educational leadership. Demonstrating the ability to translate research into broader societal or practice-oriented relevance
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of wind turbines. Despite remarkable progress in structural health monitoring boosted by AI, purely data-driven models have no physical interpretability and poor generalization capabilities. Thus