22 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at University of Amsterdam (UvA) in Netherlands
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) with quantitative techniques (e.g., computer vision, physiological sensing, environmental monitoring, crowd behaviour analysis), as well as researching existing sources of knowledge in the literature and
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qualification) in AI (e.g., machine learning, natural language processing or computer vision); A strong scientific track record, documented by publications at first-tier conferences and journals (e.g., NeurIPS
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PhD in biology, cognitive science, or an adjacent field (e.g. biomedicine, computer sciences); A strong academic track record, including high-quality publications (quantity is less important
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Dec 2025 - 22:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 30.4 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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-funded research programme in the Netherlands that includes world-leading research institutes and private partners. You will actively interact with a vibrant community of PhD candidates and postdocs and
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: 232 holiday hours per year (based on fulltime) and extra holidays between Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; a complete educational program for
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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
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courses to follow from our Teaching and Learning Centre; a complete educational program for PhD students; multiple courses on topics such as leadership for academic staff; multiple courses on topics such as
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Learning Centre; a complete educational program for PhD students; multiple courses on topics such as leadership for academic staff; multiple courses on topics such as time management, handling stress and an
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profile: A PhD in AI, preferably at the interface of information retrieval and machine learning; Research background in generative information retrieval, with publications in the leading venues relevant