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imaging data, computer models, and statistical tools. The individual will prepare manuscripts and contribute to the development of grant proposals. The McIntyreLab currently conducts research in the areas
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modelado generativo para habla/audio / Investigation of diffusion models, flow matching, and generative modeling for speech/audio Diseño de arquitecturas neuronales para TTS y ASR / Design of neural
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efficacy, and in advanced in vivo mouse models. A wide spectrum of methodologies will be used, including standard molecular biology techniques such as DNA/RNA isolation and plasmid preparation, retroviral
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transcriptomics and multi‑omics data. You will also partner with AI experts to integrate predictive models and advanced analytics into omics workflows. You will work in an expanding team led by Dr. Masoomeh
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levels. This position offers the opportunity to work at the interface of cardiovascular biology and immunology, with access to advanced in vivo models, flow cytometry and immune phenotyping platforms, and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control . Eligibility is currently open to: U.S. Citizens; U.S. Lawful Permanent Residents (LPR); Foreign
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high information content Flow MRI datasets with physics based modelling and Bayesian inference to determine constitutive models for non-Newtonian and other complex fluids in situ. The project will
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to carry out high-performance numerical simulations using our in-house CFD code, extract physical insights from simplified flow models, and characterise synchronisation thresholds and the robustness
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priorities through applied microbiological and genomic research. The postholder will work closely with the current project researcher to analyse whole genome sequencing data, support strain characterisation
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Experience in Adult Learning: Validation of Flow in Education. Frontiers in Psychology, 12, 1-12. [16] Hong J., Lee N., Thorne J. (2024). ORPO: Monolithic Pref. Opt. without Reference Model. arXiv:2403.07691