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contributions into powerful, integrated systems that drive high-impact publications. Who We're Looking For Solid expertise in deep neural networks, especially using PyTorch Strong interest (or hands-on experience
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Mellon University is looking for a talented postdoctoral associate to interrogate the circuit dynamics underlying motivated behavior. We are looking to explore the electrophysiological properties of neural
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, ensemble Kalman filters, and physics-informed neural networks (PINNs) enforce conservation laws while fitting observations. The key is to apply the vast amount of physical insights developed in turbulence
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completion of) a Master in Ecology, Geo-statistics, Neural networks, Data Analysis, Artificial Intelligence, Soil Science, Soil Conservation, Agricultural Sciences, Environmental Sciences, Geosciences
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statement when you apply. Criteria Essential or desirable Stage(s) assessed at Hold or be close to completing a PhD in neural engineering, neuroscience, biomedical engineering, computational modelling or a
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 2 months ago
) Start to develop trainable Artificial Neural Networks for the identification of sequence patterns relevant for the function of the enhancers that harbor the respective NucAlts. Admission Requirements
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communications Computing & Networking: QuMIMO, Quantum Error Correction, Multi-partite systems, Q Network Coding, HQCNN - Hybrid Quantum-Classical Neural Networks Security & Logic: QRL - Quantum Reinforcement
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to be developed: Analyze iEEG data. Develop multimodal algorithms. Perform the characterization of the epileptogenic network. Where to apply Website https://seuelectronica.upc.edu/en/procedures/call-for
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responsibilities. Experience Essential: E1 Experience of analysing human body movement from sensor data (eg RGB videos and/or MOCAP data) using Deep Neural Networks (such as Graph Convolutional Networks). E2
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classes and their roles in scientific applications, such as deep neural networks (DNNs), convolutional neural networks (CNNs), transformer models, and graph-based neural networks. Familiarity with software