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language models, with hands-on experience in training, fine-tuning, or evaluating state-of-the-art models Solid knowledge in adversarial machine learning or trustworthy AI, including experience with
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We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data
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aspects of machine learning focusing on efficiency, generalization, and sparse neural networks. Currently we are expanding our expertise by applying our theoretical findings also to robotics. Hybrid is our
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candidate will perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon
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generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research directions and
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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities
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advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and deep learning. He/she will support the development of an improved forest RTM that can
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Machine Intelligence (CVI²) research group (CVI² Group ), led by Prof. Djamila Aouada, to pursue a PhD in Computer Vision with a focus on Media Forensics and Deepfake Detection. The candidate will conduct
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and uncertainty mapping at satellite, airborne and drone levels. You will explore advanced retrieval techniques, including spatio-temporal regularization, and hybrid methods with machine learning and
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Machine Intelligence (CVI²) research group (CVI² Group ), led by Prof. Djamila Aouada, to pursue a PhD in Computer Vision with a focus on Media Forensics and Deepfake Detection. The candidate will conduct