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Reference Number BAP-2026-500 Is the Job related to staff position within a Research Infrastructure? No Offer Description Modern embedded AI systems rely on Deep Neural Networks (DNNs) running on resource
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of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
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specific requirements: a) Experience in the application of data analysis and machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification
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will join a community of exceptional scientists working on diverse topics ranging from how organisms age or how our DNA is repaired, to how epigenetics regulates cellular identity or neural memory
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representations, and flow matching) for uncertainty-aware 3D reconstruction of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast
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further appears in the approximation of various dynamical problems by tensor networks and neural networks. In all these cases, the parametrization is typically irregular, meaning that the occurring linear
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. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position The Neural Dynamics and Computation group (www.gonzalocognolab.com), led
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optimization of machine learning methods for processing Quantum OCT (Q-OCT) signals. Design, training, and testing of neural network architectures for artefact and dispersion removal in Fd-Q-OCT and SS-Q-OCT
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, and EHR data. Experience with modern deep learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and SciPy. Familiarity with convolutional neural networks (CNNs), graph
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at the intersection of AI, deep learning, computational neuroscience, and vision science. You'll develop biologically realistic neural networks to understand how individual differences in the brain shape perception