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learning algorithms for closed-loop optogenetic control of neural circuits (DC2). The appointed DCs will participate in an international research team as part of the EU-funded Marie Skłodowska-Curie Actions
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neural networks inspired by the human brain and examine what mechanisms enable the networks to acquire human-like intelligence. For more information, please visit our lab homepage. We are currently seeking
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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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classification of Aluminium 5083 TIG welding using HDR camera and neural networks. J. Manuf. Process. 45:603–613. https://doi.org/10.1016/j.jmapro.2019.07.020 Wang R, Wang H, He Z, et al (2024) WeldNet: a
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validated at CPPM. In parallel, the candidate will improve data reconstruction algorithms by using artificial intelligence techniques (e.g. neural networks), to optimize the separation between signal and
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network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring the use of large language models to support neural network design and data preprocessing
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of abstract concepts based on prior human knowledge. Neural-symbolic AI integrates the learning capabilities of neural networks with the reasoning and representational abilities of symbolic AI, thereby enabling
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or more of the following research fields: Quantitative finance Neural Networks Financial Engineering and Risk modelling Wealth management, payment and lending AI/Machine Learning applications in financial
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optoelectronics and cryogenic device platforms in the context of artificial neural networks and neuromorphics. Information on the department can be found at: https://qdev.nbi.ku.dk/ Our research Our group conducts
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advancement of the research of deep neural networks, in the field of adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research