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design and causal inference (including virtual lab experiments); and/or (4) network or computational modeling. The ideal candidate will have a strong interest in applying these tools to questions of group
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, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool
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mesoscale fractal geometry, creating physics-informed neural network models to analyze turbulent structures, and comparing simulation results to astronomical observations to develop methods for inferring
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; this includes high-level familiarity with causal inference, experimental methods, and econometric techniques. Familiarity with public opinion research and experience working with historical sources would be
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Genetics, Biochemistry or Cell Biology, and a track record of peer-reviewed publications. Experiences with yeast genetics, epigenetics and chromatin, or fluorescence microscopy are desirable but not required
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when extending an offer. The ideal candidates will hold a PhD, have multiple years of prior research experience using a model organism, and a track record of peer-reviewed publications. Prior experience
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student and faculty innovation and entrepreneurship that make a difference in the world. Expectations An ideal candidate will: Have a prior track record of publications at standard ML or robotics venues Be
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requirements include: Strong background in wireless communication systems and networks Expertise in physical layer and MAC layer design for wireless systems Proven track record of publications in relevant IEEE
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research team including experts at NYUAD and Cleveland Clinic Abu Dhabi with a demonstrated track record in vision science, neuro-imaging, neuro-ophthalmology and deep learning. 2) Using Advanced Imaging