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that enable interpretable models to learn from deep representations. In this project, the researcher will develop theory and algorithms for (hybrid) model selection that allows to exploit domain knowledge
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elements—can function as materially driven climate regulators, providing passive environmental comfort while also advancing architectural expression. As buildings account for a significant proportion of
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the area of Actuarial Sciences, Econometrics, Mathematical Economics and Game Theory, and Operations Research. Members of the department teach in the BSc and MSc programme in Econometrics & Operations
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to contribute to the exciting new area of Differential and Probabilistic Programming? As a PhD candidate in the ERC project FoRECAST, you’ll work independently and in a collaborative, diverse team. This is a
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Programming? As a PhD candidate in the ERC project FoRECAST, you’ll work independently and in a collaborative, diverse team. This is a unique opportunity to contribute to the technological foundations
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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., high performance
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or institutional theory. Experience or affinity with active modes of doing research such as engaged scholarship, transdisciplinary research, and/or ethnography and corresponding research methods. Ability to work
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, the researcher will develop theory and algorithms for (hybrid) model selection that allows to exploit domain knowledge through interactive learning. For this we will build on the minimum description length (MDL
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investigate how 3D printed clay systems—columns, claddings, and related architectural elements—can function as materially driven climate regulators, providing passive environmental comfort while also advancing
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Council (NWO). The main goal of this project is to develop theory predictions and new searches for dark matter bound states in the ATLAS experiment at the LHC. The project will be conducted in close