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12th December 2025 Languages English English English The Department of Computer Science has a vacancy for a PhD Candidate in Algorithmic Fairness in Recommender Systems Apply for this job See
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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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and skilled individual to join our cross-disciplinary research team studying the ecology of vision. Our goal is to better understand the inter-relationships between vision, evolutionary processes
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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
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computer vision models for forest-based 3D point cloud data. In recent years, large advances have been made for deep learning algorithms for high-resolution point clouds from small geographic areas. We seek
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models for forest-based 3D point cloud data. In recent years, large advances have been made for deep learning algorithms for high-resolution point clouds from small geographic areas. We seek a candidate
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supervised deep learning algorithms for 3D laser data from forests Developing self-supervised deep learning algorithms for 3D laser data from forests Expand for a wider variety of downstream tasks focused
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university, the internationally accredited registrar and classification society DNV, and Cancer Registry of Norway. You will be analyzing and developing algorithms for privacy preserving health registry data
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biology, evolutionary biology, phycology, biosystematics) MSc thesis submitted prior to the application deadline (degree must be awarded before employment starts) Experience with molecular laboratory work
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algorithms for privacy preserving health registry data access. The goals of such access include supporting registry operations as well as health care research. Of particular interest in this context