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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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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA DELL'ENERGIA ELETTRICA E DELL'INFORMAZIONE "GUGLIELMO MARCONI" | Italy | 2 months ago
. This Research Fellow position focuses on deployment methodologies for heterogeneous RISC-V many-core systems using the Deeploy framework, addressing workload partitioning, parallel execution, and communication
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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Your Job: This PhD project bridges between classical analytical methods and modern AI based techniques to analyse spike train recordings to advance our understanding of neural population coding while maintaining clarity in the interpretation of results. Concurrently, AI-based methods are...
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 8 hours ago
systems research. The Department of Scientific Computing Core is looking for a Postdoc (f/m/d) GPGPU Computing. Your tasks # Research in GPU programming and (highly) parallel programming for HPC and
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 8 hours ago
(highly) parallel programming for HPC and Scientific Computing # Active contribution to Open Source Scientific Community codes # Working with application field scientists from various fields and different
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parallelization strategies Experience with porting and optimization of parallel HPC software for modern architectures including GPUs Excellent knowledge of a variety of suitable computing languages (including C
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with electronic structure methods (DFT, TD-DFT, BSE). Experience with scientific software integration and user-facing tools. Knowledge of HPC or parallel computing. Experience with machine learning in
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and resolves performance and scalability issues in large-scale workflows (e.g., optimizing parallelization, data chunking, and storage efficiency for high-volume datasets).
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, where variations in edge-state bandwidth enable sensing mechanisms that outperform conventional approaches while remaining robust to defects. In parallel, valley phononic metamaterials have been proposed