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to decompose the task into generating individual 2D horizontal layers separately in order to save GPU memory resources. Your Qualifications / Experience: completed MSc university degree in mathematics
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manufacturing. Your work will capture compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change within a GPU-accelerated solver to reduce simulation
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We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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of spikes by a model Develop proxy apps representing the different processing stages of spiking network simulation code (targeting CPU and accelerators such as GPU or IPU) Systematic benchmarking of proxy
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University invites applications for a full-time, on-site PhD position. The position is funded through the LUMI AI Factory (EuroHPC) and focuses on scalable AI-for-science workflows using GPU-accelerated
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program embedded in a large-scale, nationally funded research consortium with access to unique multimodal clinical datasets - State-of-the-art GPU infrastructure for training and fine-tuning large
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role We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training
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of advanced language models and derived use cases by focusing on one or more of the following topics in their PhD project: Training and inference of ML models on GPU clusters. Method development for scalable
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(OMOP CDM, FHIR) or metadata harmonisation Experience with ETL tools, workflow engines, or bigdata frameworks (e.g., Spark, NiFi, KNIME) Familiarity with containerisation (Docker) and HPC or GPU computing