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Algorithmic Categorization”, 2024.07707.IACDC supported by measure “RE-C05-i08.m04 – “Support the launch of a program of R&D projects aimed at the development and implementation of advanced cybersecurity
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis
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of Antwerp. See also: https://remotesensing.vito.be/news/sspirit-tackling-plastic-pollution The main task of the KU Leuven PhD project will be the further development and validation of a two versatile
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Assessment Systems: Toward Trustworthy AI for Complex Educational Evaluation Image and Video Analysis Using Machine Learning Algorithms Mathematical and Computational Neuroscience, from neural data and network
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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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candidate will contribute to: Developing supervised deep learning algorithms for 3D point clouds Developing self-supervised deep learning algorithms for 3Dpoint clouds Expand for a wider variety of downstream
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contribute to: Developing supervised deep learning algorithms for 3D point clouds Developing self-supervised deep learning algorithms for 3Dpoint clouds Expand for a wider variety of downstream tasks focused
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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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-body positron emission tomography (PET) with magnetic resonance imaging (MRI). In conjunction with the development of algorithms, dedicated software and hardware-based simulations will be developed