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situated in the field of machine learning. Potential research topics include, but are not limited to, algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine
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to extract knowledge from data, modelling large-scale complex systems, and exploring new application areas in data science. Areas of interest include but are not limited to models and algorithms for knowledge
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computing, networked systems, and beyond. The work will range from theoretical and algorithmic development of distributed protocols and coordination mechanisms, through the design and implementation
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support the teaching activities courses at KTH and further develop methodologies and algorithms for the quantum computer simulators. Qualifications Requirements A graduate degree or an advanced level
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, system-wide efficient, as well as fair for heterogeneous participants. Addressing these challenges requires new mathematical models and algorithms that blend optimization, game theory, and control with
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settings and in quantum serverless computing environments. The work will range from theoretical and algorithmic development of compilation and scheduling techniques, through the design and implementation
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techniques to model fluid turbulence, fusion plasmas (with a particular focus on inertial confinement fusion target design), and quantum circuit simulators. The work will range from algorithmic and theoretical
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can take one of two directions depending on the expertise of the selected candidate: novel algorithm design, with advanced control, optimization and deep reinforcement learning; hardware-oriented
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cutting-edge systems design, AI at the edge, optimization, and shaping future mobile networks, this is your chance to dive in. A strong focus will lie on the development of optimization algorithms
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topology, algorithms and complexity, combinatorics, differential geometry and general relativity, dynamical systems, mathematical physics, mathematical statistics, number theory, numerical analysis