13 parallel-processing-bioinformatics uni jobs at Lawrence Berkeley National Laboratory
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. Collaborate with physicists, computer scientists, mathematicians and engineers across LBNL divisions to define software requirements, implement robust solutions, and develop software for high-energy particle
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methodology Assist with code optimization and integration into Department of Energy (DOE's) applications running on the exascale computer systems with GPU accelerators We are looking for: PhD or equivalent
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, barcoding, documenting, and processing incoming materials. The position works with vendors and internal teams, managing a wide range of goods through internal systems (iBox, Peoplesoft, and Ebuy) Candidates
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policies in coordination with Employee and Labor Relations. Directly supervise key administrative functions, including administration, safety, communications, and budgeting. Business Services and Process
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governance and review processes. Content planning and administration duties In collaboration with the Director of Communications, develop and manage editorial style guidelines to ensure consistent, high
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, and manufacturing processes will focus on creating Work Instructions and Test Plans. Students with a CAD and design focus will work on 3D modeling, design, and creating manufacturing drawings. Training
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architectures for science Developing and advancing extreme-scale scientific data management, analysis, and visualization Developing and advancing next-generation machine learning, AI, and data science approaches
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of world-class science. You will perform varied tasks essential day-to-day operation of the plant facilities systems, complete preventive maintenance, make small and complex equipment repairs, monitor and
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worldwide. In this role, developers create and operate robust, mission-critical solutions that collect, process, store, and present network measurements and event data using open-source and cloud-native
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work and business processes, safeguarding the Laboratory's long-term "license to operate" under the 'GOCO' model and mitigating institutional risk to levels acceptable to the Department of Energy (DOE