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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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demonstrating a proof of concept for low-power logic gates based on spin waves. The work involves experimental research. The post-doc will be responsible for: - the growth and optimization of magnetic thin films
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system optimization for remote construction. This position will focus on advancing research in construction assembly science and technology, logistics optimization, and real-time communication frameworks
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systems. The individual will be responsible for: • Develop and implement models for the structural and mechanical performance and optimization of mass timber systems, using data-driven approaches
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advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more of the following research areas: Development of efficient numerical linear
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network models. Of particular interest are candidates who have a background and/or interest in one or more of the following: optimization and optimal control (esp. optimal control of partial differential
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, and network optimization for 6G networks and FutureG wireless networks. Successful candidates will have the chance to work with top-notch researchers from both academia and industry on future wireless
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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) for municipalities; Optimizing building-level energy demand and supply Extend and customize the frameworks according to the research questions and project needs. Conduct scenario analyses to explore pathways