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on Macro, Labor and/or Development Economics. The appointment is for 2 years and will begin September 1, 2026, subject to final budget approval and UAE visa acquisition by the prospective post-doctoral
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on accelerator developments towards the HL-LHC. Expertise in trigger development, performance and optimization and/or the ATLAS computing model is preferred. The selected candidate will work in the ATLAS
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development of cognitive omni-band wireless systems for 6G and beyond. The successful candidate will be jointly supervised by Mahmoud Rasras and Murat Uysal and will work in close collaboration with other
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
management. Demonstrated research excellence through peer-reviewed publications. Experience with programming languages and software platforms relevant to Digital Twin development (e.g., Python, MATLAB, Revit
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Description The Trabolsi Research Group at New York University Abu Dhabi invites applications for a research scientist position focused on the development of covalent organic frameworks (COFs)-based
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, methodology development, total synthesis of natural products, photoredox or electro-catalysis, and computational mechanistic studies. We are seeking highly motivated individuals who, beyond technical excellence
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: Statistical analysis & causal inference Data management, collection & visualization Social network analysis & graph theory Online tool/web development Running controlled experiments Game-theoretic modelling
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theory, visualizations, and online tool development. Experience in conducting online controlled experiments is also desired, but not required. Excellent communication skills in English, ability to work in
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statistical analysis, data management and collection, causal inference, network analysis, graph theory, visualizations, and online tool development. Experience in conducting online controlled experiments is
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candidate is expected to take a leading role in the development of the group tasks and help in supervising PhD students. Applicants must have a PhD in theoretical high energy physics or related field