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Field
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or related varieties. Familiarity with high performance parallel storage systems, such as Lustre, GPFS, HA cluster, Pacemaker, Corosync, etc. Experience with programming using C/C++, R, or Fortran, and use
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part of QA program. Logs will also be reviewed to ensure accurate entry for labels. (3%) Prepare and organize specimens for permanent storage. Blocks are regularly pulled from storage and problems will
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libraries using parallel synthesis techniques, X-ray crystallography, or chemical biology In-depth knowledge and hands-on experience with a wide range of state-of-the-art techniques involving enzyme assays
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with researchers, understand research problems, and build the skillset to contribute to computational research through code. If you have an interest in scientific programming, high performance computing
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for individuals with a passion for AI research, strong programming skills, and a desire to work at the intersection of engineering and scientific discovery. The role will also provide teaching and training
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well as the effects of electrical stimulation on neural signals and cognition. The successful candidate will work on multiple projects in parallel. The candidate will also supervise other researchers including
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in accordance with grant agreements and accounting standards. Partner with department research administrators to gather supporting documentation and understand program/research activities related
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international members with 12 faculty and over 500 students in the B.S. and M.S. programs. Our interests span both research and practice, including artificial intelligence, big data, bioinformatics and
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provide technical support for supported systems. Plan, design, test, debug, implement, maintain, secure, troubleshoot and document systems and processes in accordance with client and/or department timelines
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, Michigan Drug Discovery (MDD) is a campus-wide program created to support and fund drug discovery research at the University of Michigan. MDD provides ongoing funding for drug discovery projects and provides