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Field
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, and the mathematical and computational foundations of neural networks. Familiarity with the following areas is meritorious: machine learning, computational complexity, tree automata and tree
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neural networks on synchronised video, audio, and text data to automatically detect how gestures or gaze contribute to meaning in conversations.- Bridging cognitive science and AI to model how humans
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leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and
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, CTR) Design and implement physics-informed deep neural network architectures which accelerate finding electron bunch shapes self-consistently for large measurement campaigns Implementation and
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convolutional neural networks by exploring transformers, implicit neural representations (INRs), and hybrid architectures that integrate physical priors such as periodicity, symmetry, and long-range correlations
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural recordings Familiarity with neuroanatomy and neurophysiology Knowledge of dynamical
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models to collaborate, adapt, and forget outdated knowledge intelligently, and a DFG-funded project on distributed optimization and scalable training of deep neural networks, including transformer
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knowledge acquisition (machine learning), the complexity of learning algorithms, pattern recognition with artificial neural networks, computational geometry and information processing in biological neural
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Quantitative MRI is a PICNIK with Physics-Informed data Consistent Neural Implicit K-space