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pruning), carbon-aware computing, minimizing algorithmic complexity, maintenance requirements, mapping energy efficiency and related aspects using KPI (key performance indicators) with respect to ESG
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for teaching activities. About SURE-AI SURE-AI is a Norwegian AI centre funded by the Research Council of Norway (2025-2030). The primary objective is to create a new generation of algorithms
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, privacy, and resilience. Today’s Transformers models scale poorly and assume abundant cloud resources. The research program FIND aims to deliver architectural and algorithmic breakthroughs that enable
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Network project called MINDnet. The project consists of 15 PhD students at 7 universities, one research center and two companies. The project has partners from eight different EU countries. All 15 PhD
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Network project called MINDnet. The project consists of 15 PhD students at 7 universities, one research center and two companies. The project has partners from eight different EU countries. All 15 PhD
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and participation in international conferences. Since you will be part of the research center, which consists of researchers with different backgrounds, you are expected to participate and contribute
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
Thermography. This raw dataset is needed to be processed and annotated to train supervised and unsupervised AI models. The research will aim to develop deep learning algorithms for damage classification
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order to be successful, you bring: MSC in Computer Science, Physics, Engineering, mathematics or related disciplines with a strong background in data analysis, mathematical modeling and algorithms Good
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-term ambient monitoring will capture operational vibration patterns due to anthropogenic and natural excitation. The models will help interpret how different damage scenarios affect the ambient vibration
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how new technologies and emerging modes of working in the digital economy influence experiences, management practices, and forms of knowledge production across different empirical contexts. Possible