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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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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
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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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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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, Neuroscience, or a related field. A strong background in functional neuroimaging with experience in decoding and/or encoding models is required. Candidates with experience with recurrent neural networks will be
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, concentration and functional inequalities • Mathematical aspects of machine learning and deep neural networks • Free Probability aspects of Quantum Information Theory. While excellent candidates with other
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modelling and simulation of complex systems reinforcement learning or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging
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intelligence, including reinforcement learning, computer vision, and deep neural networks applied to robotics. Track record of peer-reviewed publications and active participation in interdisciplinary research
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, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus
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field Demonstrated expertise in deep learning and neural networks Strong publication record in top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICML, etc.) Experience with multimodal learning or cross-modal