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Field
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of Robotics and Industrial Informatics (CSIC-UPC) offer a position to work on World Models for Human Behaviour Anticipation https://ramonllull-aira.eu/archivos/theme_field/world-models-for-human-behaviour
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: The development of computational models to represent structural performance using commercial and research software tools The development and validation of machine learning and artificial intelligence models focused
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working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems
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humans in playing board and computer games, driving cars, recognizing images, reading and comprehension. It is probably fair to say that an artificial neural network can perform better than a human in any
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of computer vision models is comfortable working in multidisciplinary research environments, where methods are applied to biological or real-world problems Qualifications The applicant must: hold a PhD in a
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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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22 Jan 2026 Job Information Organisation/Company Faculty of Law University of Rijeka Research Field Technology » Computer technology Researcher Profile Recognised Researcher (R2) Positions PhD
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candidates will be invited to a practical task in addition to an interview. If you have any questions, please contact: Univ.-Prof. Dragoș Ciobanu, PhD Professor of Computational Terminology and Machine
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models