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data (e.g., synchrotron X-ray microtomography), Design and use of autoencoders (VAEs, GANs), diffusion models, and other ML methods for analyzing and discovering patterns in probability distributions in
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essential tool for training and testing of AI models and control systems for robots and autonomous vehicles. In a digital environment, large amounts of annotated training data can be created safely and easily
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this, we focus on self-supervised denoising, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural
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, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural networks (CNNs), which identify local
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essential tool for training and testing of AI models and control systems for robots and autonomous vehicles. In a digital environment, large amounts of annotated training data can be created safely and easily
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at industrial partners at TRL 6. Our objectives: Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and electrode-structure to encounter optimum electrolyte as
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the microscale up. The developed technologies will be validated in half-cells and full working batteries at industrial partners at TRL 6. Our objectives: Multiscale modelling to better understand RFB behavior and
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well as to reduce other negative environmental impacts, are central to the work. The work will build on the extensive knowledge and model structure developed within phase 1 of the research program Mistra Food Futures
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to this third-cycle studies which corresponds to four years. Position description We are seeking a PhD student to join our research team specializing in the analysis and modeling of multiphase flows. The research
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. Work duties The main task is to conduct research. Teaching may also be included in the duties. Work in this field includes the design, modelling, realization, and characterization of nanophotonic