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solutions to reduce power consumption in neural networks. In this project you will be involved in a collaborative effort investigating neuromorphic mixed-signal/near-analog circuits for next generation edge
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spaceborne remote sensing. You will first identify large-scale drivers of compound extremes in models and observations, then build an emulator using advanced AI methods, such as convolutional neural networks
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learning to reconstruct perceived information from functional MRI in real time. For more information on Professor Norman’s lab, see https://compmem.princeton.edu. Questions should be directed to Professor
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | about 1 month ago
interactions that proved powerful in other contexts. Hundreds of candidate variables can be combined together using a range of machine learning approaches, based on graph theory and on neural networks (in
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skills in Python, Java, etc. ● Expertise in machine learning, neural networks, and deep learning ● Excellent writing and communication skills ● Highly motivated, ability to identify potential problems and
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The Laboratory on Social & Affective Neuroscience is located in the Department of Psychology at Georgetown University. We conduct research aimed at understanding the social, cognitive, and neural basis
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trustworthy AI architectures in domains such as generative AI, large language models, neural networks, and imaging as well as to integrate various types of data to advance research and improve clinical decision
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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Learning, particularly Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us