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maps. Knowledge graphs can be used to model these transformations and to link geodata sources to questions. In this project we will apply symbolic and sub-symbolic AI methods to scale this up across
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answer maps accordingly. We use knowledge graphs to model these transformations and apply AI methods to scale them up across large map repositories, enabling users to explore many ways maps can be reused
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workflows, turning geodata into new answer maps accordingly. We use knowledge graphs to model these transformations and apply AI methods to scale them up across large map repositories, enabling users
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workflows, turning geodata into new answer maps. Knowledge graphs can be used to model these transformations and to link geodata sources to questions. In this project we will apply symbolic and sub-symbolic
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learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential inference algorithms as well as proofs of their correctness and efficiency) and systems (e.g
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transformations, transformative social innovation, and social movement theories is especially relevant. Practical experience with community organising and/or social design is welcome. Competencies: You have a good
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transformations, transformative social innovation, and social movement theories is especially relevant. Practical experience with community organising and/or social design is welcome. Competencies: You have a good
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science and societal application. We contribute to innovative information technologies through the development and application of new concepts, theories, algorithms, and software methods. With our expertise
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of both science and societal application. We contribute to innovative information technologies through the development and application of new concepts, theories, algorithms, and software methods. With our
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collaborative, diverse team. This is a unique opportunity to contribute to the foundations for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic