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computing, data science, and cyberinfrastructure environments supporting large-scale research workloads (Required) 2. Demonstrated knowledge of scale-out, parallel, distributed, object, and federated storage
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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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Program at the University of Michigan Taubman College of Architecture and Urban Planning seeks applicants for a one year position at the rank of Lecturer I in the architectural history curriculum
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technical support for research projects. Familiarity with shared memory and distributed memory parallelism (e.g., OpenMP, MPI, OpenACC), accelerators (e.g., GPUs), and large-scale file systems. Proven
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and epigenetic manipulation of AML cells and massively parallel reporter assays to investigate gene regulation in leukemia. Computational analysis of these data will involve applying existing tools and
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and cloud computing platforms. Formulating necessary solutions using various parallel computing paradigms and tools, HPC schedulers (such as slurm), Containers and Kubernetes, Python, Bash and other
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/8/Stream, Ubuntu), experience with SELinux is a plus Parallel and distributed file system (Lustre) experience is a plus, as well as knowledge of Ceph Scripting in BASH, Python Understanding of basic
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systems, code generation and transformation, HPC, parallel and distributed computing, compiler infrastructures, heterogeneous systems, and autonomous system optimization. Desired expertise includes
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. Documenting system administration procedures for routine and complex tasks. Technical Environment: Linux build automation in a large, distributed computing environment with Puppet/Ansible/Git/Docker; scripting
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. Working knowledge of coding in a distributed computing environment, including basic familiarity with parallel processing concepts and research workflows that use distributed or multi-node computing