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. The role involves contributing to this research project with a focus on model development, implementation, and testing. Further tasks involve dataset curation, analyzing results, and the creation
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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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, optimization, reinforcement learning, and estimation. You will mostly work on mathematical modelling, theory development, algorithm implementations and simulations, but the work may also cover physical
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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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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 continuation as a researcher at Ericsson Research. Practical work tasks include: Developing algorithms and models for dynamic spectrum sharing using RDT data Implementing and evaluating signal processing and
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in the network. Here unfair indicates that people with different personal traits are differently and unjustly affected by algorithms not designed to consider those traits. This project aims to develop
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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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of the Mathematical Insights into Algorithms for Optimization (MIAO) group at the Department of Computer Science at Lund University. The research group, which is headed by Jakob Nordström , is also active
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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