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Theory, Representation Theory, Arithmetic Geometry or related areas * Excellent command in English is required. Good working knowledge of French would be desirable but not required We offer * A modern
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, conversational text, and biometric data. The core challenge is utilizing self-supervised representation learning to construct a continuous latent manifold of individual differences. Responsibilities ● Develop
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detection and monitoring, invariant and distributionally robust representation learning algorithms, and deployment-time calibration with uncertainty quantification using approaches such as conformal
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( https://github.com/WolframInstitute ) * Contributions to writing projects ( https://writings.stephenwolfram.com/ ) * Academic representation and collaboration Qualifications * PhD in
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Knowledge Graphs for Defence and National Security AI) funded by The Turing Grand Challenge on Defence and National Security: https://ernestojimenezruiz.github.io/projects/guard/ . The postdoctoral
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Knowledge Graphs for Defence and National Security AI) funded by The Turing Grand Challenge on Defence and National Security: https://ernestojimenezruiz.github.io/projects/guard/ . The postdoctoral
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Description We have an opening for a Postdoctoral Researcher in Explainable AI to contribute to fundamental R&D on understanding what modern AI models learn and how that knowledge is represented internally. As
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representation, optimisation, generalisation, robustness and reliability, while remaining sufficiently tractable to inform engineering practice. A key objective is to transform theoretical advances into
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authorship in top-tier AI, machine learning, data science, computer vision, natural language processing, knowledge representation, statistics, applied mathematics, or robotics conference or journal papers
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Postdoctor in Mathematics Reference number PAR 2026/748 The University of Gothenburg tackles society’s challenges with diverse knowledge. 58 000 students and 6800 employees make the university a