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Field
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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
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the application of machine learning and artificial intelligence. By using neural networks developed in Python, the project aims to generate robust and generalisable models for scaffold design. Industrial
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applied to control problems or tiny RL scenarios. Explore digital hardware realizations of the proposed RL algorithms within existing spiking neural network chip designs. Quantitative comparisons with
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. Advanced modeling techniques, such as surrogate modeling, machine learning, and physics-informed neural networks, will be applied to accelerate simulations and enable real-time performance. A strong emphasis
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efficient, adaptive use of multistable networks, offering an alternative to energy-intensive digital systems with rigid bits and separated memory and computation. In this project, we aim to design and realize
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respect to an infinitesimal perturbation of the dataset, provide a rigorous framework to: - **Identify the most informative samples** among the predictions of a deep neural network (DNN), with the goal
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-film solar cells.” You will become part of an enthusiastic team working closely with collaborators at DTU Physics and DTU Nanolab to advance neural network-based methods for materials discovery. Project
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), artificial neural network (ANN)) will be applied using the parameters of strongest influence on the target properties. Moreover, the obtained data will be fed into a generative pre-trained transformer model
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely