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Do you want to contribute to groundbreaking research in the development of a theoretical framework and numerical algorithms for evolving stochastic manifolds? This is an exciting opportunity for a
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microscopy, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran
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processing algorithms, build a Dash-based GUI, and develop standardized analysis pipelines. The role includes community-oriented tasks such as documentation, tutorials, and user support. The work requires
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large
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variety of cutting-edge research in time-domain astrophysics, including the development and implementation of machine learning, statistical and data-driven algorithms to study exotic transient phenomena
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, fined tuned for zooming in on machine spatial reasoning, is within the scope of this project. Developing efficient algorithms for converting computer simulations of a system in a complex environment (e.g
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in Ithaca, NY with a focus on developing deep learning algorithms. Dr. Haiyuan Yu, Ph.D. is a Tisch University Professor of Computational Biology in the College of Agriculture and Life Sciences and a
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part