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well as large-scale GPU computing facilities for deep learning. We are looking for a Research Engineer to manage the EEE GPU Cluster. The role will focus on enhancing the EEE GPU Cluster team’s ability in terms
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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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modeling). Experience working with HPC/GPU resources and job schedulers (e.g., Slurm) and/or cloud-based deployments. Track record of contributing to peer-reviewed publications as a computational specialist
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Master degree in Informatics Website for additional job details https://www.unimc.it/it/ateneo/bandi-e-concorsi/finaziamenti-ricerca/borseattiv… Work Location(s) Number of offers available1Company
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FLAME-GPU accelerated agent-based modelling of material response to environmental and operational loading EPSRC CDT in Developing National Capability for Materials 4.0, with the Henry Royce
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Job Code 0005 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The successful applicant will assist in the adaptation of the PPMstar code to run well on GPU-accelerated
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, Cloud Service Deployment). Desired: Experience with High-Performance Computing or GPU programming (CUDA). Specialized knowledge of Neural Rendering (NeRF/3DGS) or Satellite Photogrammetry. Demonstrated
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA INDUSTRIALE | Italy | 12 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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frameworks (e.g., PyTorch). Familiarity with GPU-accelerated environments, virtualization tools, and prototyping using real testbeds (e.g., SDR). We expect a diploma in computer science or telecommunication
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community. We provide the resources to match your ambition: Industrial-Scale Computing: Exclusive access to massive GPU clusters and high-performance computing. Guaranteed Talent Pipeline: Generous