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Defence Academy invites applications for a fully-funded postdoctoral position in the interdisciplinary area of AI-driven scenario forecasting. This position is in collaboration with the Data Science Center
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Defence Academy invites applications for a fully-funded postdoctoral position in the interdisciplinary area of AI-driven scenario forecasting. This position is in collaboration with the Data Science Center
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demonstrable interest and experience in probabilistic and/or other modelling approaches to sensorimotor control. You have good social, communication and organisational skills. You are a team player. Experience
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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form of topic modelling as a method for automated content analysis. Topic models are a probabilistic method that allows organizing the contents of a text corpus into a set of semantically coherent
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air laboratory to uncover how sub kilometer air–lake coupling influences convection, rainfall and heat budgets. The insights will advance coupled models and improve forecasts in vulnerable regions. As a
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novel combinatorial, algebraic, and probabilistic tools, you will work on resolving fundamental extremal problems in the area of finite geometry. These problems have connections to Ramsey theory, coding