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(IRT) models in small samples. The ideal candidate has prior knowledge of IRT models, a basic understanding of common estimation methods, and strong programming skills in R, Python, or another relevant
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, immunoprecipitation and confocal microscopy. Experience with cancer organoid models and/or bioinformatics is an advantage. We offer broad training possibilities in the required experimental methods within a stimulating
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) and economics (or related fields). Applicants must have experience in one or more of the topics: Model-predictive control Numerical optimization Econometrics Virtual power plants Power systems and/or
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analysis. This digital transformation has also paved the way for innovations like AI-assisted morphological analysis. This project will research a self-produced AI model for automatically classifying plasma
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generative AI models are replicated and perhaps exaggerated in the output of generative AI, and that this could lead to culturally specific narrative biases. Applicants are required to submit a detailed
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management expert and a research assistant. AI STORIES explores the hypothesis that deep narrative structures in the datasets used to train generative AI models are replicated and perhaps exaggerated in
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or mediated by digital tools, which partake into complex collaboration ecologies. The rise of intelligent technologies, such as generative AI tools, has placed the spotlight on new forms of collaboration
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how people estimate quantities. The project combines theoretical modeling with behavioral experiments to advance our understanding of the cognitive processes that connect our sense of magnitude with
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, who is leading studies on metabolism and signaling, closely collaborating with Prof. Ines Heiland, who is an expert in metabolic modeling. Active participation in and contribution to joint project
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analysis. The candidacy period is three years from the start date and the desired start date is 01/12/2025. The research group, Experimental Studies of Complex Human Behaviour (ESCOHub), has an overarching