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research, machine learning or artificial intelligence (e.g., large language models, EHR foundation models), causal inference (e.g., target trial emulation), and child health research. The research program
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skilled in object-oriented coding (preferably Python) and data analysis; affinity with machine learning and explainable AI techniques, preferably in a geoscience context; good social skills. As a university
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven
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documents uploaded using the dedicated electronic form. helpdesk: petra.koudelova@fsv.cvut.cz Machine learning for 3D printed multifunctional metamaterials Description: The PhD topic focuses on the use
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., Zheng, G., & Srikumar, V. (2017). DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning. Proceedings of the ACM Conference on Computer and Communications Security (CCS). [6] Kwon
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transferability; (ii) sample-inefficient learning requires large volumes of annotated, domain-specific data; and (iii) complex architectures with tightly coupled components hinder modular adaptation. To address
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In this project, the selected candidate will join us in conducting research in statistical learning, developing data-driven methods to learn models of large-scale signals and systems from data
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distinguished research and teaching profile in quantitative methods, machine learning (ML) and Artificial Intelligence (AI) applications in the Energy field, and experience translating these cutting edge
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resonance imaging data among other imaging modalities, machine learning methods for prediction, treatment effect estimation, and contribute to understanding brain biomarkers of Alzheimer’s disease and their
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student projects). Qualification You should have a background within deep learning, big-data, computer vision, or related fields, as well as experience in in-line process monitoring or similar areas