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We are looking for a motivated PhD candidate in the social and behavioural sciences with a quantitative background for the project “Technostress amongst teachers in primary education”. The aim
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to contribute (~10% of total workload) to the teaching and supervision of Bachelor and Master students. The position will be supervised by Prof. Nima Monshizadeh and Prof. Bayu Jayawardhana, and the candidate
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interactions with the system operator are essential. Apart from the research project, the candidate is expected to contribute (~10% of total workload) to the teaching and supervision of Bachelor and Master
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of the department. Typical teaching duties involve assisting students during (programming) labs, assessing students work and (co-)supervising BSc and/or MSc graduation projects. In this role you will apply and master
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Representation Learning. Causal Explanations. Causality and Large Language Models. Counterfactual learning. Requirements Specific Requirements Master's degree in Computer Science, Mathematics, or a related field
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of transparency, accountability, principal-agent relationships, flypaper effects, moral hazard, and more. In this PhD position, you will contribute to bridging that gap between theory and practice through empirical
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) currently has a vacant PhD position as part of the Artificial Intelligence: Power Asymmetries and Data Justice project led by principal investigators Dr Lonneke van der Velden and Dr Claudio Celis Bueno. ASCA
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Asymmetries and Data Justice project led by principal investigators Dr Lonneke van der Velden and Dr Claudio Celis Bueno. ASCA is one of the five Research Schools within the Amsterdam Institute for Humanities
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Phd Position in Multiscale Spatiotemporal Modelling of Microbiome During the First 1000 Days of Life
will be responsible for constructing, simulating, and validating computational models of the microbiome in oral and gut regions, as part of the MetaHealth consortium project. The primary aim
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combine it with extensive simulation studies. As currently defined, it will rely on three main methodological domains: (1) active learning, (2) causal machine learning and (3) policy evaluation. It is an