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, energy consumption, and accuracy.; ; Training deep learning models, especially in LLMs, faces critical challenges that compromise the optimal use of GPUs. These bottlenecks result in poor computational
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, OpenCV), GPU computing (e.g., CUDA), SLAM and point cloud processing (e.g., PCL). Familiarity with ROS-based development and Linux software tools is considered a fundamental asset. (30
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main activities: ; 1. Exploration of the applicability of eBPF and its ecosystem: review and exploration of the use of eBPF in different domains (e.g., GPU), of the various libraries available for its
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including heterogeneous accelerator devices such as GPUs, DSPs or FPGAs, requiring software to cope with concurrent and parallel synchronous and asynchronous computation. The emergence of connected autonomous
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techniques that enable the application of eBPF to areas that currently lack direct support (e.g., GPUs, HPC systems, etc.); 2. Development of new eBPF functionalities: exploration and development of new
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including heterogeneous accelerator devices such as GPUs, DSPs or FPGAs, requiring software to cope with concurrent and parallel synchronous and asynchronous computation.; The emergence of connected
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in rivers or estuaries with applications to plastic transport. The transport models will be included in existing shallow-water solvers running in hybrid CPU-GPU architectures. The expected workplan
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, considering discrete modulation; Contribute to the implementation of digital signal processing algorithms in a FPGA platform; Contribute to the implementation of information reconciliation algorithms in a GPU
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- Competitive stipend under the Portuguese FCT MSc grant scheme; - Access to GPU clusters and cloud resources; - Travel and equipment budget for conferences, training, and research visits; - Active involvement in
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(LLMs); Configure and optimize cloud computing solutions or on-premise infrastructures that ensure high availability and scalability; Implement tools for efficient resource management, such as GPU