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Field
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defect centers. This project aims to explore the use in-situ x-ray diffraction techniques available at the newly-upgraded Advanced Photon Source synchrotron at Argonne as a means to gain new insights
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that will be employed include flow cytometry, confocal and electron microscopy, in vivo two photon imaging, behavioral studies and transcriptomic techniques. The successful candidate will join the Center
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circuit modeling, in vivo two-photon microscopy of synaptic and cellular activity, two-photon optogenetic perturbations, CRISPR/Cas9 gene manipulations, and rodent visual behaviors. In
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healing, and nuclear mechanosensing. The successful candidate will have established experience in the following areas: (a) imaging techniques such as fluorescence, confocal, and multi-photon microscopy, (b
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research and education with a focus on wireless electronics, photonics, quantum technology, as well as bioelectronics and microelectronics packaging and integration within Chalmers University of Technology
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by the Department of Plant Physiology and supervised by a multidisciplinary team with complementing expertise in cell-cell adhesion and photonic techniques to study biological processes. Specific
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
including in vivo whole-cell patch-clamp recording, two-photon calcium imaging, or behavioral assays Special Physical/Mental Requirements N/A Special Instructions For information on UNC Postdoctoral Benefits
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employment is contingent upon availability of these funds in the future. Required Education and Experience: PhD in Physics, Optics, or Photonics Required Knowledge, Skills, and Abilities: Strong publication
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platforms, with collaborations with the Argonne Leadership Computing Facility and the Advanced Photon Source. This postdoc will contribute to the design and implementation of a software infrastructure for the
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The Advanced Photon Source (APS) at Argonne National Laboratory invites applications for a postdoctoral position focused on developing novel computational approaches for multi-modal biomedical image