23 machine-learning-"Computer-Vision-Center" PhD positions at Utrecht University in Netherlands
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computational lens. This calls for strong expertise in computational methods, machine learning, and data modelling combined with solid knowledge of music. We particularly aim to cover a broad range of musical
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organizational skills. Experience with using machine learning packages (e.g.PyTorch). Completed academic courses in AI or machine learning. Interest in societal, ethical and philosophical questions. We consider it
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., high performance, functional array programming DSLs) to tackle challenging probabilistic and differentiable programming applications (e.g., experimental design, machine learning for science). We do so by
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machine learning packages (e.g.PyTorch). Completed academic courses in AI or machine learning. Interest in societal, ethical and philosophical questions. We consider it an advantage if you bring one or more
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering
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- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute to a corpus of geo-analytical scenarios with
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on behavioral lab experiments but may also include other empirical approaches depending on how the project develops. During the PhD, you will: Learn how to combine theory-driven empirical sociology with
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, including abstract geospatial workflows; design AI- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute
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generate new knowledge and critically assess approaches that integrate scientific insights with artistic research to address climate justice. JUST ART PhDs will study and develop concrete cases to learn how
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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