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, volcanology, critical raw materials, and machine learning / AI. The network combines advanced petrological observations and multimodal analytical data with modern ML (including physics-informed and generative
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, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way. Examples include mapping liquidity and volatility features to a
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project The main objective of this PhD project is to explore and analyze bio-inspired neural architectures for early detection from spatio-temporal data under realistic sensing and computational constraints
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information exchange (HIE) Natural language processing in clinical/biomedical domains Mobile health, digital health, human–computer interaction in health Learning health systems, community health informatics
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application! We are looking for a research engineer within the Division of Statistics and Machine Learning (STIMA) at the Department of Computer and Information Science. In this position, you will have the
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and computational physics High-energy physics/astrophysics/cosmology Statistical mechanics/complex systems/non-linear physics Machine learning/first-principles calculations/large-scale simulations
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or industry equivalent work at a computing facility, or using/managing HPC resources Experience working with large scale machine learning models Experience with performance optimization, debugging, and
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of areas, including AI and machine learning, cloud and mobile computing, computer system and information security, evolutionary computation, computer vision and graphics, and bioinformatics
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pipeline for learning from a large-scale microscopy dataset. You will work with expert computational scientists, data engineers, and experimentalists to train models that learn foundational embeddings
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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in