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bioinformatic pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation and, in some instances, clinical reporting
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as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale and high-performance storage, and
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. Computational Infrastructure: Deploy and maintain high-performance computing environments (GPU clusters, cloud services) for large-scale image-text experimentation. Data Engineering: Establish workflows
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computing (HPC) systems, including CPU, GPU, storage, file systems, networking, visualization, job schedulers, and scientific applications Experience leading the implementation and execution of research
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(CNNs, vision transformers), multi-GPU multi-node training, and federated learning strongly encouraged. Experience working with cloud platforms. Experience with high performance computing. Publication
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). Experience managing systems utilizing GPU (NVIDIA and AMD) clusters for AI/ML and/or image processing. Knowledge of networking fundamentals including TCP/IP, traffic analysis, common protocols, and network
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interfaces (APIs), servers and other web resources open to the broader scientific community. Support the on-site HPC Linux GPU cluster with Slurm queuing system. Maintain RAID NFS storage, Lustre storage
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systems. Areas of interest may include, but are not limited to: - Advanced accelerator chip technologies, such as GPUs or other specialized chips for large-scale AI processing - High-speed memory
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interfaces (APIs), servers and other web resources open to the broader scientific community. Support the on-site HPC Linux GPU cluster with Slurm queuing system. Maintain RAID NFS storage, Lustre storage
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research on wireless sensing and communications, and a GPU Lab for training of advanced machine learning models. IDLab is both part of the University of Antwerp and the research centre imec. The University