65 parallel-computing-numerical-methods "Simons Foundation" Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI
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of existing and emerging networks and communication systems, with a possible starting date in January 2025 (or later). The group’s research builds upon the areas of system, network, information, and computer
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machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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Experience in machine-learning modeling for solid mechanics applications Experience in the development and coupling of numerical methods for solid mechanics modeling Experience in digital rock technology
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Description The New York University Abu Dhabi Computational Approaches to Modeling Language (CAMeL) Lab seeks to hire a post-doctoral researcher to work in any of the lab research areas, to be
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
Geotechnical Engineering, Civil Engineering, or a related field, and should demonstrate strong expertise in at least two of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian
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United Arab Emirates Application Deadline 1 Oct 2025 - 00:00 (UTC) Type of Contract Other 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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Arab Emirates Application Deadline 1 Oct 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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United Arab Emirates Application Deadline 1 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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subfields will also be given full consideration. Advanced methodological training is essential; this includes high-level familiarity with causal inference, experimental methods, and econometric techniques
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geometry is a plus. Knowledge of safe control methods including Lyapunov stability, barrier functions, and certified learning frameworks. Hands-on experience with robotic manipulators (preferebly Franka