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. This post is funded by UKRI’s Moving IMPACT Project (Integrated Means to Power Agriculture, Clean Cooking and Transportation https://movingimpactproject.org/) and will investigate innovative power
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 10 days ago
include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 10 days ago
creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search, interpretation
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 1 month ago
accurately study the selected algorithms, participate in the development and maintenance of the PEPit (https://pepit.readthedocs.io/ ) and AutoLyap (https://github.com/AutoLyap/AutoLyap/ ) software packages
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at ISS-La Princesa. - Train laboratories with diagnostic and/or surveillance functions in the analysis of omics data from the research groups of IIS-Princesa. - Deliver cutting-edge algorithmic solutions
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numerical methods for quantum many-body systems, (iii) correlated electronic structure theory, (iv) tensor network algorithms. Applications should be submitted by March 1, 2026 for full consideration
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. Through continuous structural monitoring systems Through continuous structural monitoring systems and artificial intelligence algorithms (machine and deep learning, hybrid physical-data driven methods
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algorithms for Robust Relative Biological Optimization, considering uncertainties in the parameters of dose-response models. Finally, the aim is to o assess the potential of fractionation optimisation in
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed