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responses approximate human behavior. The project involves a collaboration between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain
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an interest in how psychological theory can improve synthetic data and in deepening our understanding of when and why LLM-generated responses approximate human behavior. The project involves a collaboration
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to exploit some of the cutting-edge experimental and computational methods, comprising constraint-based and kinetic modeling, statistical analysis of large datasets, high-throughput metabolomics, time-lapse
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to support a circular bioeconomy. We are seeking a highly motivated postdoctoral researcher to develop and apply machine‑learning and data‑science methods, with a focus on computer vision, to enhance wood
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, technology and society using frameworks and methods from Science, Technology & Society (STS), history, philosophy, anthropology, critical theory, and the arts. We privilege interpretive, qualitative methods