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molecular material. Three major research programmes will run in parallel: (1) reconstructing the regulatory architecture of E. coli at high resolution as a foundation for predictive models of gene
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other parallel file systems Familiarity with Slurm, Spack, and scientific software environments Experience with hardware provisioning and out-of-band management technologies such as BMC, IPMI and
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all of these skills on the job: Experience with parallel file systems (GPFS, Lustre) Experience with Ceph Storage Clusters Experience with versioning systems and CI/CD workflows such as ArgoCD
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released models Make evaluations run quickly and at scale: parallel execution on Alps, efficient use of inference backends, caching, and result tracking Reduce mismatch between evaluations during
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coordinating an EU consortium is a plus. You are organized and reliable with budgets, deadlines, and reporting, and you keep several projects moving in parallel. You communicate clearly in English, in
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/aarch64 platforms Familiarity with HPC networking and communication stacks: Slingshot, libfabric, NCCL and its debugging Experience with parallel filesystems (e.g. Lustre) and storage performance tuning
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, computer vision, signal processing, or condition monitoring Strong programming skills in Python and experience working with experimental data Strong interest in combining mechanical modelling
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collecting and analyzing content from websites. This includes scraping structured and unstructured web content, storing and managing large datasets, applying statistical and natural-language processing methods
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-related trade studies covering processing capacity, memory, timing, bandwidth, autonomy, reliability, and fault tolerance. Develop real-time, event-driven, autonomous, synchronization, and GNC
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in the quantum domain using trapped ions focussing on quantum information processing, quantum metrology, and fundamental questions in quantum physics. Our research is driven by a combination of