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- University of Amsterdam (UvA); Published yesterday
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-edge computational infrastructure, and structured methodological support will foster impactful research. The ADORE programme encourages cross-disciplinary learning, international collaboration, and
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is embedded in an international academic–industrial collaboration and targets fundamental questions in end-to-end autonomous driving and neural view synthesis. Your work is expected to lead to
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of justice, pluralism, sustainability, and care, our work is aimed at creating spaces for collaboration, critical analysis, reflection, and learning. We are an equal opportunity employer and value diversity
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-empowering AI systems. The project spans domains such as healthcare, mobility, education, law, ethics, and public governance. This position is a collaboration between Utrecht University and the University
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times of societal crisis. You will join a vibrant academic community and benefit from interdisciplinary collaboration across social psychology, sociology, and public health. As part of a collaborative
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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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to the adaptation of the Environmental Noise Directive for these new technologies. Your main focus will be to develop machine learning-based drone noise models that will be able to generate an accoustic footprint
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colleague who: holds a Master’s degree in mathematics or computer science; has a solid foundation in category theory; is familiar with dependent type theory; is enthusiastic about learning advanced category
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to understand how weathering contributes to sediment production (in collaboration with Dr. Martha Cary Eppes, UNC Charlotte). In addition, you will learn to apply the probabilistic debris-flow model SedCas
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Social Science, or related field; has strong affinity with the study of families and economic inequality; has experience with both quantitative and qualitative research (or is motivated to learn both types