10 Statistics positions at Lawrence Berkeley National Laboratory in america in United States
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interdisciplinary methods and tools appropriate to the topic at hand, including primary data, economic, and statistical analyses; modeling; and survey and interview-based research. The Energy/Environmental Policy
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. Experience in data acquisition and statistical analysis. Notes: This is a full time 1 year, term appointment with the possibility of extension or conversion to Career appointment based upon satisfactory job
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. Position requires: 1. Mathematical statistics and data analysis fundamentals including: large scale data mining and modeling; 2. Operations research, stochastic optimization, machine learning, queuing theory
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with analysis codes such as Python. Experience in data acquisition and statistical analysis Notes: This is a full time 1 year, term appointment with the possibility of extension or conversion to Career
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, Biogeochemistry, Molecular Biology and an understanding of key and complex biological concepts including genes, pathways, and microbial phylogeny. Experience with UNIX utilities and filesystems, R statistical
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concepts including genes, pathways, and microbial phylogeny. Experience with UNIX utilities and filesystems, R statistical analysis platform, and standard bioinformatics tools and databases. Excellent oral
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prototype detector systems. Design, set up, and execute experiments to characterize and evaluate detector performance. Analyze experimental data using advanced statistical and data analysis techniques, and
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performance. Analyze experimental data using advanced statistical and data analysis techniques, and develop custom data analysis tools and algorithms as necessary. Prepare high-quality manuscripts
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interdisciplinary research team. Desired Qualifications: Experience with standard bioinformatics and statistical methods and tools (examples include sequencing databases, assemblers, and aligners, programming
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/frameworks (e.g., TensorFlow, PyTorch). Proficiency in field-programmable gate array (FPGA) programming in RTL design and verification. Strong background in ML, statistics, and mathematics, with practical