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PhD Position in Causal Agent-based Modelling of Complex Social Systems Faculty: Faculty of Science Department: Department of Information and Computing Sciences Hours per week: 36 to 40
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. Examples of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world
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evolution and social origin of human languages de novo and in silico. We do this by combining novel experimental paradigms and computational models, including group communication games, agent-based
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of the PhD student based at CWI in Amsterdam will study integrated hydrogen-electricity markets. In particular techniques from Artificial Intelligence and multi-agent systems for modelling new types of markets
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. These frameworks will: Identify the causes and responsible agents of liveness breaches, and assess any resulting harm. Support recovery strategies using counterfactual reasoning (“what if?”), guiding programs back
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PhD Activating Heritage as a Mediator for Dialogue and Belonging in an Era of Polarization (1.0 FTE)
sustainability in society and active agents shaping narratives. By collaborating with heritage sites and the resources of cultural institutions, leveraging their collections, material culture, and local stories as
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PhD Position: Activating Heritage as a Mediator for Dialogue and Belonging in an Era of Polarization
sustainability in society and active agents shaping narratives By collaborating with heritage sites and the resources of cultural institutions, leveraging their collections, material culture, and local stories as
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the form of, for example, agentic systems or other abstract AI-pipelines, but research has shown that existing frameworks are brittle. If successful, though, such systems can uncover high-value insights from
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of vaccines and antiviral agents. These studies often involve interdisciplinary collaboration combining virological, veterinary, pathological, and immunological expertise. Your tasks will include: • Preparing
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. The integration of Knowledge Graphs (KGs) with AI agents will link the data and the actions taken by AI agents. Reinforcement Learning from Human Feedback (RLHF) will enable AI to learn and adapt based on real-time