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general approaches from cognitive science (resource-rational analysis) to investigate in how far LLMs may acquire “causal world models”. The focus of the project is on the latent representations in LLMs
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: Develop and complete a doctoral dissertation aligned with work package 1 objectives Requirements: Master’s degree or PhD in Oceanography, Climate Science, Physics, Biogeochemistry, Earth System Science or a
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: Develop and complete a doctoral dissertation aligned with work package 1 objectives Requirements: Master’s degree or PhD in Oceanography, Climate Science, Physics, Biogeochemistry, Earth System Science or a
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to collaborate across disciplines PhD level: Strong interest in biodiversity, ecosystem resilience, and Earth system science Experience working with ecological or environmental datasets Marine background desirable
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, or a related field. strong interest in marine biodiversity, Earth system science, and interdisciplinary research Ability to work with complex ecological or environmental datasets experience with data
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institutions, and a provider of research and development for companies throughout the world. The INM is an institute of the Leibniz Association and has about 250 employees. The INM research department Structure
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institutions, and a provider of research and development for companies throughout the world. The INM is an institute of the Leibniz Association and has about 250 employees. POSTDOCTORAL POSITION ON REVERSIBLE
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Senckenberg – Leibniz Institution for Biodiversity and Earth System Research (SGN), headquartered in Frankfurt am Main, is seeking to fill the following position at the Senckenberg Biodiversity and
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Universität München TUM School of Engineering and Design Department of Aerospace and Geodesy Earth System Modelling Group Prof. Dr. Niklas Boers Lise-Meitner-Str. 9 85521 Ottobrunn Data Privacy Notice: When
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adaptive systems that jointly optimize morphology and control for real-world physical interaction. Requirements Ph.D. in Computer Science or a related discipline. Strong background in Machine Learning