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Field
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Jozwik lab studies visuo-semantic cognition combining cognitive science, neuroscience, and computational modelling. The lab's research has focused on probing specific visual dimensions in the context
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of data scientists, software engineers, and experimental researchers on topics including: Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM
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at the national level. Through a combination of extant personal oral stories and archival sources, together with new digital humanities representations of data about infection and vaccination rates, this research
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trigger redesigns across multiple groups. The challenge is compounded by the fact that each discipline uses different data models and representations, making system-level interdependencies difficult
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established practices? iii) Technology Assessment: To what extent do representational approaches such as ontologies or knowledge graphs meet the identified requirements, and where might alternative or hybrid
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tools without disrupting established practices? iii) Technology Assessment: To what extent do representational approaches such as ontologies or knowledge graphs meet the identified requirements, and where
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statistics This PhD project falls under the collaboration between Research Thrust RT2 Physics-based models, and Research Thrust RT3 on representation, compression, learning, and inference. For long-distance
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modeling, knowledge representation and automated reasoning Knowledge of or experience with knowledge-based engineering and system engineering Knowledge of or experience with some of AI frameworks and
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Experience with deep learning for image analysis and/or medical image processing Knowledge of self-supervised learning, representation learning, and/or generative models Experience with multimodal machine
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art history, excellent English and or German, good computer skills (knowledge of a programming language would be advantageous). Extent of Employment: 30hours/week. Job grading in accordance with