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Summary Post-Doctoral Positions Atomic Physics/Quantum Information Indiana University, Bloomington Multiple post-doctoral positions are available in the Trapped Ion Quantum Information group at Indiana
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Postdoctoral Research Scholar of Physics and Astronomy- Physics and Astronomy-Galaxy Evolution and Galactic Archaeologyition University of Oklahoma Norman Campus: Dodge Family College of Arts and
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2021, the Centre for Genomic Regulation (CRG) received the renewal of the'HR Excellence in Research ' Award from the European Commission. This is a recognition of the Institute's commitment to developing
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. The Postdoctoral Research Associate will work primarily with Professor Qian Yuan and Professor Benchun Duan to develop and employ algorithms to link mantle convective processes with earthquake ruptures. The position
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required You will hold a PhD in Algorithmic Game Theory, Computer Science, Operational Research, Mathematics, or a related discipline. You must have evidence of a developing research agenda with a developing
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healthcare access and outcomes for high-risk patients. The postdoctoral researcher will play a key role in translating machine learning algorithms into professional software with an intuitive user interface
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undergraduate students during summer months. Apply existing remote sensing based algorithms, such as 3T, to estimate latent heat fluxes from thermal imagery and soil water balance. Develop and test upscaling
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undergraduate students during summer months. Apply existing remote sensing based algorithms, such as 3T, to estimate latent heat fluxes from thermal imagery and soil water balance. Develop and test upscaling
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Biology in Cancer Genomics and Immunology. This position will be involved in the development and/or application of computational approaches to understand the mechanism of cancer development, progression
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supervision conferring with higher levels only in unusual situations. 8. Performs other job-related duties as required. Responsibilities: -Develop and implement advanced numerical algorithms and deep learning