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: NYU - NY: Tandon School of Engineering Description Visualization Imaging and Data Analysis Center (VIDA) at NYU’s Tandon School of Engineering is hiring a Postdoctoral Research Scholar We are looking
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of the research grant when extending an offer. Qualifications Ph.D. in Neuroscience with experience with in vivo 2-photon imaging. Application Process This institution is using Interfolio's Faculty Search
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Post-Doctoral Associate in the Division of Engineering (Electrical and Computer Engineering) - Dr. Farah Shamout New York University: NYU - Global: Abu Dhabi: AD_Engineering: AD_Electrical and
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sponsored project on advanced manufacturing. The candidate will be jointly supervised by Prof. Nikhil Gupta of Mechanical Engineering and Prof. Ramesh Karri of Electrical and Computer Engineering departments
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of cardiovascular medical devices, non-invasive imaging modalities such as echocardiography, and computational flow simulation software), are required. For consideration, applicants need to submit a cover letter
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, or computer, or mechanical engineering, or related field to apply. A strong publication record is encouraged and previous experience in areas such as Distributed control and optimization, Swarms, Autonomous
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electrophysiology and imaging approaches. If needed, access to other experimental techniques and animal models can be provided through collaboration with other laboratories. The Post Doctoral Associates (trainee) is
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these processes regulate metabolic health and disease. We employ a range of scientific tools such as genetic mouse models, single cell and spatial transcriptomics, imaging, and various molecular and cellular
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that will be developed and fabricated during our project. This system will use an imaging system derived from smartphones and enable the safe and secure uploading of data to a cloud‐based repository. Together
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applications (e.g. databases, algorithms, visualizations). Processing one or more data types/signals (e.g. text, audio, images, physiology). Applying machine learning algorithms (e.g. regression, neural networks