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Field
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: Comparative genomics RNA secondary structure prediction Covariance models and RNA homology search methods Machine learning or artificial intelligence applied to biological data Transcriptomics analysis methods
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malignancy. The project integrates mouse and iPSC models, CRISPR genome editing, molecular biology, flow cytometry, and single-cell multi-omics to dissect disease mechanisms. We are committed to mentoring
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AWI - Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research | Bremerhaven, Bremen | Germany | 2 months ago
Qualifications and Skills Experiences in numerical modeling, advanced data analysis, and physical oceanography; Experience working with Earth System Models or ocean circulation models. Further Information Contact
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can use relevant numerical and programming tools to develop, analyse and validate models and control schemes, and you are comfortable working with measurement data for model refinement and performance
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cutting-edge methods, models and technologies in environmental science, quaternary sciences, bedrock geology, paleontology, physical geography, biodiversity and ecosystem science, remote sensing, Geographic
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About the Opportunity Summary: Research involves developing and implementing material models to predict microstructure, phase change and residual stress in processes in high energy processes in
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processing. 3, Demonstrated expertise in the modelling, design, and optimization of acoustic transducers and metamaterials, with a solid understanding of acoustic wave physics and numerical simulation methods
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 17 hours ago
and contract staff. You will learn how to do the following: Design and validate numerical reservoir models for unconventional enhanced oil recovery processes in shale and tight formations. Calibrate and
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. Duties/responsibilities Performing high level theoretical, and numerical research and data analysis (55%) Presentation publication, and proposal preparation (20%) Mentoring PhD and MSME students (10
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to develop, implement, and apply novel computational techniques to problems of scientific importance, in collaboration with application scientists and engineers. Topics of interest include data-driven modeling