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of biased decision-making in machine learning (ML)? This PhD position offers a unique opportunity to contribute to cutting-edge research in algorithmic fairness, ensuring that automated decision-making
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biased, outdated, or sensitive data? That's where the project TRAI comes in. This research project aims to develop machine unlearning algorithms to selectively erase specific knowledge from trained AI
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critical literacy skills to evaluate news in the context of algorithmic personalization and GenAI. It brings together expertise on media and journalism studies, digital literacy and inclusion, argumentation
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multiscale analysis of the mass distribution, as well as that of the flow field structure, and of the force and tidal field that has been shaping the cosmic web. The basic detection algorithms to infer
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and algorithms for efficient deep-tissue energy transfer. Experimental Validation – Integrating and testing prototypes in realistic laboratory and clinical settings to demonstrate safe and effective
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our software development team, developing novel scientific algorithms and applications in the areas of spectroscopic analysis and mining of the science data catalogues extracted from the pipelines
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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, including local hydrogen and electricity markets, but also flexibility markets related for managing network congestion. Some specific topics that are relevant for this PhD position include (non-exclusive list
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industrial partners and is partly externally funded by the KK Foundation. In co-production with our corporate partners and the community, we develop concepts, principles, methods, algorithms, and tools
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Computer Engineering or related areas; Knowledge of the development of automatic [deep] learning and information visualization applications; Experience in the use of algorithms and data analysis methods