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for data analysis and modeling. Familiarity with analyzing large-scale healthcare datasets and real-world data. Experience in developing and applying simulation models, including system dynamics, agent-based
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. Validation of new types of markets (both those designed above and others) through principled multi-agent simulations, complex systems analysis or other data-driven simulation methods. Fundamental AI techniques
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that would give you an advantage) Experience in computational modelling (e.g., agent-based Bayesian models, cognitive learning models, machine learning, robotics). Experience in annotation software such as
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interact to shape an entire simulated organ. To calibrate and parametrize the models, you will have access to data from experimental collaborators (Boxem Lab). In turn, your models will help our experimental
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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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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