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- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
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is not a standalone concept and has close connections to diversity, transparency and bias. In this position, the PhD candidate will work on algorithmic fairness in job recommender systems
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This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters in Trondheim. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will...
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collaborative, vibrant, and growing research community including 12 Associate Professors and Professors, 23 PhD candidates, researchers, and postdocs, and 4 engineers. As a PhD Candidate with us, you will work
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environmental change. The BEE section is a collaborative, vibrant, and growing research community including 12 Associate Professors and Professors, 23 PhD candidates, researchers, and postdocs, and 4 engineers
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, cargo, harbors etc. Large and deep AI models can be built using these data sets and machine learning, which can be combined with real-time satellite-based AIS data and sensors such as radar and algorithms
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of this particular PhD fellowship is to develop innovative applications, tools and models for AI game-based learning. Further, in collaboration with a postdoc, the candidate should establish models, frameworks, and
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to develop state-of-the-art multi-agent communication protocols and innovative educational technologies based on it. Further, in collaboration with a postdoc, the candidate should establish models
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postdoc, the candidate should establish models, frameworks, and technologies to create interactive, and adaptive learning experiences, and evaluate these. Duties of the position The candidate will be a
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of the collected data. This main goal of this particular PhD fellowship is to develop innovative applications, tools and models for AI game-based learning. Further, in collaboration with a postdoc, the candidate
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the fundamental limits of quantum error correction (QEC) while concurrently advancing efficient decoding algorithms for quantum error-correcting codes in the near-term, noisy intermediate-scale quantum (NISQ) era