17 distributed-algorithm-"Meta"-"Meta"-"Meta" positions at KTH Royal Institute of Technology
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, and energy efficiency—and meeting these demands will require smart, distributed computing built directly into the network. This research project focuses on designing AI-native edge computing systems
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of molecular dynamics algorithms in GROMACS. The main focus will be on mixed precision techniques as part of the GANANA EU-India HPC partnership. This R&D work will involve: Design and development of mixed
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will develop high-performance quantum software technologies, with a focus on quantum compilers, scheduling and orchestration on parallel computing systems, including in distributed quantum computing
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position within a Research Infrastructure? No Offer Description Project description Third-cycle subject: Computer Science We are looking for two highly motivated individuals to pursue a Ph.D. in algorithms
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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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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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topology, algorithms and complexity, combinatorics, differential geometry and general relativity, dynamical systems, mathematical physics, mathematical statistics, number theory, numerical analysis
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communications and networks Beamforming and MIMO algorithms Millimeter wave communications Terahertz band communications Visible light communications Channel modeling and/or interference modeling Beam tracking and
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problems and generative models. This PhD project investigates how generative AI models, particularly diffusion models, can be used as prior distributions in Bayesian inverse problems. The aim is to develop