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Field
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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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compute, for use in labs, factories, farms, homes and inspection settings. Durham offers strong facilities: Bede HPC (128 GPU), GPU cluster (90+ GPU), LiDAR, RADAR, drones, cameras, embedded devices and
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machine learning, computer vision, robotics, efficient inference or embodied AI, prioritising practical, reliable systems. Durham offers strong facilities: Bede HPC (128 GPU), GPU cluster (90+ GPU), LiDAR
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and HPC/GPU systems, with version control (git) and reproducible workflows (conda or containers, Snakemake or Nextflow).•Able to work independently as well as within an interdisciplinary, international
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, or GPU programming is an asset LanguagesFRENCHLevelGood LanguagesENGLISHLevelGood Research FieldEngineering » Electronic engineering Additional Information Selection process Applications must be sent
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, IEEE IV and RadarConf. For your research, you will have access to extensive computing resources at TU Delft, ranging from personal workstations and shared GPU servers to the Delft AI Cluster and the
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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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shared GPU servers to the Delft AI Cluster and the DelftBlue supercomputer. Your supervisors will be Prof. Dariu Gavrila and Dr. Julian Kooij. Job requirements We are looking for a candidate with: An MSc
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collation, mixed precision, multi-GPU; experiment tracking (W&B or MLflow), configuration management (Hydra), strict seed and artefact reproducibility. Graph machine learning. PyTorch Geometric or DGL
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research software and working with high-performance or GPU computing environments. Experience publishing or contributing to scientific articles or conference papers. Personal qualifications Good