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. The project focuses on high-precision optical computing for neural network applications and leverages a radically new digitized optical computing architecture. You will play a central role in advancing
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-on experience developing generative models Is highly proficient in PyTorch and/or JAX Has experience training large-scale neural networks on HPC or GPU clusters Has experience with representation learning and
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-on experience developing generative models Is highly proficient in PyTorch and/or JAX Has experience training large-scale neural networks on HPC or GPU clusters Has experience with representation learning and
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and physical verifications (DRC and LVS) using Calibre and Quantus. Knowledge of neural networks and neuromorphic systems is a strong advantage. Team player with strong collaboration skills. All
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields