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background in human or monkey electrophysiology. Studies will include simultaneous recordings and stimulation from multiple, interconnected brain regions. The researcher will gain experience with the use
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Global Critical Zone Science Chair to develop and conduct a research program to better understand forest nutrition and nutritional stress in Eucalyptus forest stands in Brazil. Research context: Forests
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of Computational Biology, or related fields Strong publication record in leading international journals Excellent background in molecular biology Excellent programming skills Experience in computational analysis
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generative design tools such as HEEDS, and/or the Dakota or RAVEN uncertainty quantification tools. Experience with FORTRAN, C, and/or C++ applied programming. Knowledge of Python, Java, or other scripting
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background in human or monkey electrophysiology. Studies will include simultaneous recordings and stimulation from multiple, interconnected brain regions. The researcher will gain experience with the use
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. Experience in parallel programming (MPI, GPU, etc.). Proficiency in biostatistical methods. Ability to work independently and in group settings. Ability to learn quickly and apply new analytic techniques. Job
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Scholar/Fellow positions is to provide individuals, who have recently (within the past 6 years) completed a doctoral degree, with a full-time program of advanced academic preparation and research training
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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers
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acquisition (DIA), data dependent acquisition (DDA), and parallel reaction monitoring (PRM) proteomics experiments to fit the specific experimental needs of stakeholder cancer researchers. Manage a wide variety
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, Bioinformatics, Computer Science, Mathematics, Statistics, Data Mining, Parallel Programming, Supercomputing, or Cloud Computing. 3) Experience collaborating with diverse and geographically distant teams