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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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be completed during the 2026 and 2027 annual periods as specified in the project proposal. Where to apply Website https://sede.udc.gal/services/electronic_board/EXP2026/004389 Requirements Research
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-field code, written in the Cuda C language and parallelized on a single GPU (Graphical Processor Unit). A parallelization on multiple GPUs would be a welcome development during the thesis. Where to apply
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INFORMATICA - SCIENZA E INGEGNERIA | Italy | 7 days ago
) that enable understanding how global behaviours emerge from local interactions. In parallel, the project may include survey activities aimed at mapping the state of the art on multi-scale methods, emergent
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efficiency Optical neuromorphic computing is emerging as a promising alternative to classical electronic architectures, offering advantages in terms of speed, energy consumption, and parallelism. Nonlinear
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Appointment Term: Open-ended Appointment Start Date: ASAP Group or Departmental Website: https://hinshawlab.stanford.edu/ (link is external) How to Submit Application Materials: Please submit your application
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The University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 5 days ago
and relevant work experience. Preferred Education Skills and Experience: Experience in High Performance Computing related technologies such as schedulers, parallel computing, storage systems
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | 1 day ago
or Engineering Where to apply Website https://www.iisgm.com/ofertas-de-empleo/ Requirements Research FieldOtherEducation LevelBachelor Degree or equivalent Skills/Qualifications Minimum job requirements: The
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involve developing an approach that uses Knowledge Organization (KO) metadata and ontologies to optimize parallel processing and scheduling policies (via Kubernetes) for Machine Learning tasks. The fellow
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given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel