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autoencoders, robust PCA, wavelet transforms). · Experience in methods to disentangle and integrate data sources (e.g., InfoGAN, β-TCVAE, TopDis / Topological Disentanglement, Independent Component
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utility for a varying range of use cases and data types. The candidate will perform the work together with an interdisciplinary team of postdoctoral researchers who are experts in the field. In general, the
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expansion of the composites and explore them as new copper current collectors for Li-ion batteries (with graphitic, graphitic+silicon based slurries or as Li Metal anodes). You will work in (Belvaux) and have
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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning