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mindset and intellectual curiosity to strengthen and complement the research profile of the Mathematical Insights into Algorithms for Optimization (MIAO) group at the Department of Computer Science at Lund
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the classical and parameterized settings. The goal is to develop general tools that can provide efficient algorithms for a wide range of graph problems. Who we are looking for The following requirements
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to pioneer novel research opportunities enabled by one of the brightest sources in the world, ii) developing AI+Physics end-to-end reconstruction algorithms that will enable a new regime of spatiotemporal
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, develop theory and algorithms for their practical use, and study complexity and performance trade-offs in relevant applications. The project is led by Professor Erik Agrell (IEEE Fellow), whose
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costs, and tightly bridge the gap between software and hardware design. As a doctoral student, you will work on developing a framework that connects new learning algorithms with their physical
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the world, ii) developing AI+Physics end-to-end reconstruction algorithms that will enable a new regime of spatiotemporal hierarchical characterization. The project is mainly computational with
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). Completed courses in signal processing, radar or communication systems. Communication skills in Swedish are valuable, but not required. What you will do Develop radar signal processing and algorithms
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, data structures, and data analysis. The research team develops algorithms and data structures with provable guarantees, by leveraging theoretical insights to obtain state-of-the-art practical algorithms
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execution of experiments, and discuss development of eg. user sample environments or analysis code for nanoprobe experiments As a scientist, you are ready to perform scientific research or nanoprobe method(s
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the development of stochastic models for decentralized energy markets, decentralized and learning-enhanced market clearing algorithms, and fair-by-design pricing strategies. The research will address one or more of