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investigate how children and adolescents use AI-based tools in situations of uncertainty, with a particular focus on age-related developmental differences in recognizing when external support is needed
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field. Strong foundations in machine learning and familiarity with current AI tools and practices. A solid understanding of large language models, in particular their reliability, security, and
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cohort of doctoral researchers and benefit from ReDiLEEP training in response diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation
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contemporary challenges in the control of learning, the project will further investigate how children and adolescents use AI-based tools in situations of uncertainty, with a particular focus on age-related
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DNA damage at single-base resolution. Building on these newly established tools, we integrate complementary multi-omic approaches to characterize DNA damage in the context of genome organization and