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design and in silico validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning
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, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally
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are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish
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. Demonstrated experience applying machine learning and AI-based approaches to empirical disease, ecological, or biological datasets, with an emphasis on pattern identification, prediction, or spatial risk mapping
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measurement quality issues related to respondent non-compliance in ecological momentary assessment or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models
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or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The appointment is a full-time position and is made for a period of three years