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Field
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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
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) modules into safety-critical embedded systems (autonomous vehicles, drones, industrial and medical devices) raises major safety and security concerns. These modules, often based on deep neural networks
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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the design and analysis of such models. PhD position 1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD
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? Then check out the vacancy below and apply for a PhD position in this exciting research direction. Join Us! Modern deep learning is progressing fast. Yet even the most advanced neural networks are paired
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neuromorphic circuits. We will also simulate high-efficiency spiking neural networks (SNN) and build neuromorphic sensory systems to validate performance and explore broad biomedical and other potential
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. In association with the SFB 1436, Neural Resources of Cognition (supported by the German
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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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invite applications for an appointment as a PhD student in EEG Foundation Models. The position is funded by the Independent Research Fund Denmark grant titled: “Decoding neural signatures of social