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Research Associate, Cellular and Molecular Medicine Posting Number req27032 Department Cellular & Molecular Medicine Department Website Link https://cmm.arizona.edu/ Location Tucson Campus Address 1501 N
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
transportation challenges in collaboration with global industry partners. The full job description is available, here: https://www.dropbox.com/scl/fi/7g4rlzhx85n9a9dwdf7ec/CTL-Postdoctoral-Associate.pdf?rlkey
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expression and purification pipelines to yield high-quality, stable viral and host targets. Optimizing and evaluating biochemical small-molecule library screening assays using purified protein targets
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al. Nature Genetics 2020, Costea et al. and Schoeberl et al., Mol Cell 2023, Mol Cell 2025). For a list of ongoing projects and publications, please visit: https://www.kcl.ac.uk/research/pavri-group
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at the Department of Astrophysics, in particular Univ.-Prof. Dr. João Alves and Dr. Stefan Meingast . Where to apply Website https://iventajobdata.eu/bestmedia/img/2496968/2000123/cl/3e9d72f2e748bae2b0855
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cell models, retinal organoids, and animal models, creating a unique translational pipeline from gene discovery to therapy development. The successful candidate will join a growing research programme
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intracranial EEG data from animals. Performs advanced signal processing and time–frequency analyses of neural recordings. Develops and implements computational pipelines using MATLAB, Python, or similar
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sets using cell ranger pipeline and additional analysis tools is desired. Must have writing experience; manuscript, grant and/or abstracts for scientific meetings Must be able to manage multiple and
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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (www.foylab.xyz/) https://foylab.xyz/ , developing machine learning, computational, and mathematical models for improving
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 6 days ago
aperture radar and LiDAR-derived vegetation characterizations into multimodal geospatial learning pipelines. These capabilities will be applied to Earth observation foundation models. The participant will