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Inria, the French national research institute for the digital sciences | Grenoble, Rhône-Alpes | France | 7 days ago
algorithms, participate in the development and maintenance of the PEPit ( https://pepit.readthedocs.io/ ) software package, including adding functionalities related to parallel optimization
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 7 days ago
core of the project is the exploration of leveraging task-based parallel and distributed programming techniques in the DDF-pipeline software components with the aim to accelerate such processing on high
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), synchronization, scalability and Amdahl's law, Flynn taxonomy, vector processing and parallel computing architectures. Reference: https://artsci.calendar.utoronto.ca/course/csc367h1 Estimated
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is BTH's largest department, with just over 70 employees. The department conducts research in computer science, covering the subfields of big data and AI, parallel computer systems, visual and
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element models. The activities will include the implementation and optimisation of simulation codes, including on parallel architectures and on the high-performance computing infrastructure of the
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design of tree-based models, distance functions and representations for time series, as well as asynchronous, parallel and efficient pipelines for processing large volumes of data and simulations
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analytics On-device processing and performance modeling Personalization and adaptive reasoning systems AI for Education Doctoral students will also have access to specialized courses in: Artificial
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and correct errors. · Support payroll processing activities during peak periods and critical payroll cycles. System Testing and Process Validation · Develop and execute payroll test
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processing, AI/ML-enabled radio interfaces, MIMO beamforming and channel estimation, interference management, environment reconstruction, target imaging, or radio-resource management. This is a industry
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-Universität München , using the following link https://www.efv.verwaltung.uni-muenchen.de/md4 no later than 30 September 2026 and in parallel in electronic form exclusively to the Federal Statistical Office