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to have a strong background in the foundations of machine learning. Special Instructions Required application documents include a cover letter, CV, a statement of research interests, and up to three
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(specifically PCECs). Proven experience in developing and validating numerical models (e.g., using COMSOL). Hands-on experience with programming for numerical optimization, machine learning, and data processing
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measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn
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representations. The project will also explore machine-learning approaches and efficient imaging strategies, including reconstruction of three-dimensional pore structures from radiography. By linking defect
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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). The position is subject to financing by the University of Bergen. About the project/work tasks Geometric Deep Learning (GDL) is a branch of machine learning that develops neural network models by explicitly
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enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning approaches for biomarker
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inference (otherwise known as spectral retrieval), which involves using forward models in conjuction with Bayesian or machine learning-based techniques in order to derive posteriors on parameters of interest
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on the developed models for agencies/commercial partners Supervise junior researchers and master students Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or
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in numerical analysis, partial differential equations (PDEs), and scientific computing. Solid background in machine learning theories, with specific experience in Physics-Informed Machine Learning