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the neural-network control of a tiled array of fibre lasers in a coherent beam combination architecture, for unlocking novel scaling and beam shaping capabilities in real-time. You will work at the
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research projects will be considered.) Technical expertise in machine learning and model fine-tuning – 10% Demonstrated experience with neural network training, loss function design, embedding-based models
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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with deep learning models such as autoencoders and neural networks. Experience with ecological, geospatial, or movement data (e.g., GPS telemetry). Strong oral and written communication skills, including
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geometry, temporal generalization). Computational measures of visual information (e.g., image statistics/compressibility proxies, deep network features, object/scene representations). Position Summary
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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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for spatiotemporal data (e.g., CNNs, LSTMs, Transformers, or Graph Neural Networks). Hybrid modeling: Experience with physics-informed machine learning or the integration of ML with data assimilation/multivariate
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
optimized design approaches. Run high-fidelity micromechanical simulations (finite element) to obtain the homogenised properties of a composite (ply-level) Use Convolutional Neural Networks (CNNs) to obtain
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physics-based insights with data-driven methods—such as physics-informed neural networks, surrogate models and Bayesian optimisation—to explain formation behaviour, identify early indicators of cell
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, including neural networks hardware accelerator systems. • Design, model, and simulate memristor cells and circuits tailored for AI acceleration. • Collaborate on the fabrication and experimental