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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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working closely with collaborators; familiarity with or a keen interest in developing mechanistic models of dynamical biological systems (differential equation-based modelling, agent-based modelling
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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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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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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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data is lacking. With the DataLibra project, we aim to close this gap, by developing AI models and tools for structured data (Table Representation Learning), to help organizations, of any size, domain
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with established causal models. Ultimately, you will design algorithms for causality-based analysis and counterfactual recovery of liveness violations. Information and application Are you interested in
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research department Virology is your home base for the execution of preclinical and clinical studies in viral infection models. Your qualities You are a strategic thinker with strong planning and analytical
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querying of AI models to intelligent AI agents’ concepts, through agentic workflows. These are among the prime targets of the newly approved AIEXPERT project: “An agentic, multi-layer, GenAI-powered backbone
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coding systems and ontologies, ensuring compliance with FAIR principles. - AI model innovation: select, adapt, and refine large language models (local, cluster, or cloud-based) and frameworks (Ollama