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, and adapt to human interaction. This requires rethinking both what we measure and how we design models. The PhD candidate will: Create datasets and benchmarks that capture emotional cues, conversational
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researched by another PhD candidate in the project. The developed methods could be applicable across many multi-agent coordination domains, from mobiltiy, to logistics and multi-robot systems. In this work, we
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that the developed methods are robust, adaptable, and grounded in real-world practice. You will apply advanced techniques such as agent-based modelling, quantitative resilience assessment, and risk analysis to
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-based modelling, quantitative resilience assessment, and risk analysis to simulate and optimise resilience strategies. The framework will be tested and refined through pilot studies in collaboration with
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contributing to new research methods in human-AI collaboration? Then we are looking for you! We are hiring a technically strong, creative, and socially motivated PhD candidate to join the international
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Wetsus - European centre of excellence for sustainable water technology | Netherlands | 3 months ago
operational performance. Based on feedwater composition (salinity, monovalent/divalent ion ratios, and valuable elements), you will model and design ED configurations that produce tailored concentrate streams
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. As part of this PhD, the candidate will: Conduct an integrative review of established competency models Create assessment tools (which may include use of AI tools) to measure CLMA proficiency
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(agent-based modeling, differential equations) or machine learning tools. Good programming skills in one of the following programming languages: R, Python, MATLAB, or similar; Excellent English language
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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 network data