Sort by
Refine Your Search
-
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
-
Science, Mathematics, Artificial Intelligence, or a related field; a basic understanding of AI concepts and techniques; familiarity with causal modeling, causal learning approaches, and generative AI is highly desirable
-
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
-
& Computing Sciences, Physics, Chemistry and Mathematics. Together, we work on excellent research and inspiring education. We do so, driven by curiosity and supported by outstanding infrastructure. Visit us
-
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
-
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
-
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
-
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
-
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
-
Computer Science, Mathematics, Artificial Intelligence, or a related field; a basic understanding of AI concepts and techniques; familiarity with causal modeling, causal learning approaches, and generative