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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
Emirates Application Deadline 8 Nov 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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Arab Emirates Application Deadline 13 Nov 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Description We are inviting applications for a Post-Doctoral
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23 Oct 2025 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
management. Develop and validate computational models for monitoring and predicting infrastructure performance. Collaborate with faculty and researchers across SHORES. Disseminate research findings through
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United Arab Emirates Application Deadline 6 Dec 2025 - 00:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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experience in the fields of computational mechanics and computational geomechanics. Preferred Qualifications: Experience in the following areas is preferred: Experience in modeling geomechanical systems
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, and analyzing these data using advanced statistical models and visualization techniques. The job also emphasizes the dissemination of knowledge about population health, particularly through
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
research on the integration of Digital Twins with AI/ML technologies for infrastructure lifecycle management. Develop and validate computational models for monitoring and predicting infrastructure