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National Lab's (Berkeley Lab) Applied Math and Computational Research Division (AMCR) has an opening in the QuIST (Quantum Information Science and Technology) Group for a Post-doctoral Scholar to advance
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. - Strong proficiency in machine learning, optimization algorithms, and computational modeling applied to construction systems. - Experience with designing and conducting experimental studies to evaluate
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diagnosis and patient treatment. Postdoctoral fellows will work on interdisciplinary projects within a highly collaborative environment to identify optimal combinations of TB drugs, old and new. Using high
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(CHX, 11-ID) beamline at NSLS-II seeks a postdoctoral researcher with a strong background in computational imaging, data science, or scientific computing. This position will focus on developing advanced
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using Pioneer-seq and genomics assays (e.g., ChIP-seq, ChIP-exo, MNase-seq, HiC, RNA-seq) Analyze high-throughput sequencing data and contribute to computational modeling of TF binding Outstanding
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the development and testing of new materials. The work will involve reactor design and setup with gas flow capability and process optimization. Qualified candidates should have a Ph.D. in chemistry, physics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 6 hours ago
and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. OUR VISION Our vision is to be the nation’s leading public
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the following fields is required: mathematical or computational biology, mathematical/statistical modeling, optimization, pharmacokinetics/pharmacodynamics (PK/PD) modeling, pharmacology, or related
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The Multiphysics Computations Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing high-fidelity scale-resolving computational fluid dynamics (CFD
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assessment of lymphatic flow, 2) Advancing methods for contrast-enhanced MRL, 3) Creating ideal contrast agents for MRL, and 4) generating phantom and animal models for MRL optimization and validation