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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 6 days ago
for acceleration of parallel applications in the High Performance Computing (HPC) environment, · work in the international team focused on development and optimization of parallel codes under national and
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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algorithms * Parallel algorithms and distributed computing * Parameterized complexity and structural graph theory * Random structures and randomized algorithms * Sublinear and streaming algorithms
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& Technology and Systems & Networking. The Parallel Computing Systems (PCS) group at the University of Amsterdam performs research on the design, programming and run-time management of parallel and distributed
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provide powerful tools to improve the quality and efficiency of data-driven models. In parallel to the development of data-driven models for dynamical systems with geometric structures such as Hamiltonian
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scan speed. In parallel, the fabrication of composition-graded structures requires combined control of laser power, scan speed and powder feed ratios, continuously responding to the changing process
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This PhD project is at the intersection of electromagnetism, numerical methods, and high-performance parallel computing, with application towards the design and optimisation of integrated circuits
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diverse team of PhD candidates who will focus on three key areas: Probabilistic and differentiable algorithms for machine learning; Programming language implementation for high performance computing
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Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description
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, elaborate illumination profiles, and large computational domains surpassing several thousands cubic wavelengths. Furthermore, you will contribute to adapting the solver for massive parallel processing, as