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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we
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numerical models to improve the simulation of complex multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid
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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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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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of digital signal processing algorithms. Other qualifications For the doctoral programme in question, the following are considered as other qualifications: Experience with development tools for digital
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, memory, timing, and cost are of main interest. The group members have expertise in a wide range of domains covering both hardware and software, including compilers, operating systems and algorithms
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We are looking for a PhD student to carry out ground-breaking research with the aim of developing new perovskite oxide materials for energy conversion and chemical processes. The project is
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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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is on fundamental limits, and development of algorithms and methods. Applications can be found in, for example, signal, image and video processing for autonomous vehicles and swarms of drones; massive
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification