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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods
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29 Jul 2026 Job Information Organisation/Company NEW YORK UNIVERSITY ABU DHABI Research Field Mathematics Chemistry Researcher Profile Recognised Researcher (R2) Established Researcher (R3
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-preservation algorithms. The successful candidate will develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work
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, scalability and mathematical soundness; translate abstract mathematical ideas into practical tools and modelling strategies for space-related technologies, systems analysis and mission-enabling applications
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) PhD, MD, or MD/PhD (or foreign equivalent). At least one academic degree in the field of Mathematics, Physics
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information science) as well as applied sciences utilizing quantum computers. (6) Integration of information theory, mathematical modeling and machine learning and their application to medical science problems
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zonotopes in digital circuits and the model checking of discrete-space systems, with the goal of developing methodologies and tools that make verification scalable and reliable. Your research will focus
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opportunity to work at the intersection of computational neuroscience, psychology, and applied mathematics. Core responsibilities include: Focus on decision-making, linking computational models and mathematical
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, are considered. The participant will gain experience in multi-scale computational modeling and machine learning of mechanical behavior of high temperature alloys. They will be a part of interdisciplinary team and
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methods and/or the parametrisation method knowledge of automatic differentiation techniques is highly advantageous familiarity with the mathematical modelling of biological, climate, or ecological systems