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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
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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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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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models. Geometric Deep Learning for Structural Synthesis: Leveraging Graph Neural Networks (GNNs) and manifold learning to optimise complex geometries in medical device design and advanced manufacturing
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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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at all levels throughout the institution and with geographically distributed collaborators Advanced knowledge of NLP and AI principles, including concept in computational linguistics, neural networks, deep
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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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of electronic devices and circuits: Cadence, Virtuoso, and others. Specific Requirements Knowledge: Emerging devices such as memristors. Emerging computing architectures such as spiking neural networks
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PostDoc/Senior Scientist - Process and Plant Design in the Field of Liquid Organic Hydrogen Carriers
languages (e.g., Python, Julia, Matlab) Strong interest in process modeling and simulation, including novel methods and approaches such as neural networks and nonlinear optimization Ability to analyze complex