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Smart Damage Sensing, Sustainability and Green Energy Production), a multidisciplinary research program combining polymer chemistry, functional materials, photocatalysis, artificial intelligence, and high
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Ave SE, Minneapolis, Twin Cities Campus - East-bank. Sponsorship: This position is not eligible for H-1B visa sponsorship. Qualifications Qualifications ● Required Qualifications: A PhD degree in
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by candidates within five years of receiving their PhD. In particular for this position, the following is required: PhD in data science, AI, computer science, machine learning, aerospace engineering
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are looking for candidates to have the following skills and experience: Essential criteria 1. PhD qualified in a relevant subject area, such as theoretical physics, computational physics, quantum information
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substantial freedom to shape the scientific direction of the research within this focus, in close collaboration with an interdisciplinary team of scientists. Job requirements PhD degree in Earth sciences
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Description The Center of Interdisciplinary Data Science and Artificial Intelligence (CIDSAI) at NYU Abu
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to start imminently, and before mid October this year. The project intended for the post-doc is part of a collaboration within my group on the Google Willow Chip project (https://www.bbc.co.uk/news/articles
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in probabilistic temporal event dynamics. Required Qualifications: - PhD in computer science, engineering, biomedical data science, informatics with advantage for experience in conducting research
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, understand, and communicate in English sufficiently to perform job duties. Must be computer literate with proficiency and working knowledge of database and reporting tools such as Microsoft Word, Excel, and
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that can be repurposed for viruses with pandemic potential. This includes working closely with computer scientists to utilize published “omics” datasets and machine learning approaches to identify FDA