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learning models (e.g., multimodal AI, large language/world models) with specific finetuning for ELEVATE; designing geographically context-sensitive urban design recommendations that promote active mobility
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on text and image feature learning for news ecosystems, analysing the complex multidimensional feature space of visual information to support data-driven journalism. This includes experiments
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and authorities interested in improving civic participation. The research in this postdoctoral position focuses on text and image feature learning for news ecosystems, analysing the complex
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for reasoning, on efficient and explainable machine learning for extracting and structuring information from large datasets, and on combining the two in neuro-symbolic AI.
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the perspectives of the immune system and diseasedcells. Those learnings will make it possible to improve clinical products, provide new strategies for finding targetable HLA-bound antigens and to decipher what/how
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completed) in Natural Language Processing or a closely related area. Solid knowledge of machine learning, especially deep learning. Experience in model development and/or fine-tuning. A practical mindset
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out through its integrated performance observability, applying learning paradigms for causal performance attribution and prediction, and pioneering quantum-HPC-cloud middleware to shape the next
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the perspectives of the immune system and diseased cells. Those learnings will make it possible to improve clinical products, provide new strategies for finding targetable HLA-bound antigens and to decipher what/how
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tracking, pupillometry or related psychophysiological methods, or is eager to acquire these techniques; has strong quantitative and statistical skills (preferably using R and/or Python); demonstrates