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studies. These pipelines must be capable of efficiently exploiting different types of parallelism, both at the level of a computing node (CPU and GPU) and at the level of a cluster of PCs. This environment
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distinct individuals with varied interests, needs, and abilities. Salary based on course contract. Campus varies. Minimum Qualifications University Parallel: Master’s degree in the discipline or subfield in
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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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with the nano-drones of the University’s SwarmLab The research activities will be hosted by the Parallel Computing and Optimisation Group (PCOG) at SnT, headed by Prof. Grégoire Danoy. PCOG conducts
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available. Salary based on course contract. Minimum Qualifications University Parallel: Master’s degree in the discipline or subfield in which an individual is teaching, or Master’s degree and a minimum of 18
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Future. Discover. Together. The introduction of AI applications places high demands on optical connections in data centers. In addition to the approach of fewer parallel channels with very high data
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expertise with international research infrastructure. In parallel with our research agenda, the division invests in training the next generation of scientists and clinicians by offering high‑quality
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-heavy domains. In addition to infrastructure leadership, the Chief Architect will drive research in parallel computing, distributed systems, performance engineering, and next-generation AI infrastructure
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA INDUSTRIALE | Italy | 14 days ago
Description Analysis and development of methodologies to accelerate the computation of numerical optimization through parallelization and the use of GPUs. Where to apply Website http://www.unibo.it Requirements
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anomalies in evolving graphs. In this research proposal, our aim is to explore the parallels of deep learning and anomaly detection in dynamic graphs. In particular we are interested to redesign deep neural