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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances
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. The performance of such batteries hinges on the solid booster formulation, morphology, and the efficiency of charge transfer between mediator and solid under continuous flow. Your tasks Your task is to develop
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and modeling as well as machine learning approaches, supported with strong skills in computational optimization of methods. Experience working with large-scale datasets and high-performance computing
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genetics. Our research program uses integrative genomics approaches to define the molecular mechanisms by which genetic variation shapes endothelial cell biology and confers risk for coronary artery disease
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and high-performance computing environments; • A reasonable knowledge of immunology is also desirable; • It is also desirable for the candidate to have a strong interest in modeling gene regulatory
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methods Computational or process modeling using tools such as Python, MATLAB, Aspen, COMSOL, Fluent, or OpenFOAM Experience with high performance computing environments Experience working in a classified
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Hours Per Week 38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description
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with single-cell sequencing data and preferentially with spatial omics Working in high-performance computing environments is highly preferred Good understanding of cancer biology, immunology, cell
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) synchrotron located at the Paul Scherrer Institute (PSI). You will be a core member of the SNSF Starting Grant project NEXUS-3D, working at the intersection of applied physics, high-performance computing, and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 12 days ago
proficiency in scientific programming (Python or R) and deep learning frameworks (PyTorch or TensorFlow) is recommended, alongside experience processing large spatial datasets using cloud or high-performance