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networks (CNNs), which identify local correlations in the images. However, in this project, the aim is to go beyond standard CNN-based methods by developing new approaches based on transformers, and implicit
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develops an adaptive AI-guided XR platform for capturing and transferring expert manufacturing knowledge. Your focus will be on developing AI methods for analyzing and modeling human workflows based on data
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handful of them have excited states with the opposite parity as the ground state. Hence, in most cases, traditional spectroscopic methods cannot be applied to negative ions, leaving many of them poorly
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capabilities of nonlinear quantum systems, employing tools from quantum information theory and quantum metrology. The work will involve learning and applying mathematical methods to solve open quantum dynamics
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: Help develop a non-invasive computer vision method to track and analyze how hens move in 3D space. You will gain hands-on experience in behavioural studies, animal welfare science, and innovative data
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the DC will use pore-scale direct numerical simulations (based on the lattice-Boltzmann method) to enable the precise quantification of mass transport within electrode microstructures, reconstructed via X
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novel machine learning method development. However, you will be part of a larger cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities