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Field
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focused on the challenge of accelerating ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track
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laws and symmetries into architectures like neural operators, physics-informed neural networks (PINNs), and graph-based solvers, the project aims to accelerate simulations in areas including protein
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ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track record in conducting machine learning research
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Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks (PINNs), Biologically Informed Neural Networks (BINNs), and
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diseases. The successful applicant will join a number of fascinating projects on engineering novel approaches to modulate physiology and neural activity in the brain, gastrointestinal (GI) tract, and other
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background and expertise in one or more of the following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757
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planets, and trans-Neptunian objects in retrograde motion. Traditional orbital dynamics methods will be combined with machine learning techniques, such as convolutional neural networks and Vision
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University Abu Dhabi (NYUAD) seek to recruit Post-Doctoral Associates for two projects for Rokers Vision Lab reporting to Dr. Bas Rokers. 1) Linking Retinal, Neural, and Perceptual Deficits in Visual Disorder
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neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view learning, transfer learning, and data fusion techniques to integrate heterogeneous omics datasets