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integrate physics-based simulations with experimental data and enable artificial intelligence (AI)-agent-assisted scientific workflows. This position will contribute to multidisciplinary research connecting
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. Proficiency in academic English writing (e.g., reports, papers, theses). Preferred qualifications: Experience with LLMs, multimodal data fusion, or agent-based AI systems. Familiarity with privacy
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experiments—accelerating molecule discovery. Position Summary We are rebuilding the Design-Make-Test-Analyze (DMTA) cycle, infusing scientific automation with foundation models, multi-agent systems
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experiments—accelerating molecule discovery. Position Summary We are rebuilding the Design-Make-Test-Analyze (DMTA) cycle, infusing scientific automation with foundation models, multi-agent systems
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analysis using tools such as NetworkX Graph‑based ML or graph neural networks (GNNs) is a plus Experience with modeling and simulation techniques, such as: Network, agent‑based, or discrete‑event simulation
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or coursework involving modeling and simulation techniques, such as: Network, agent ‑ based, or discrete ‑ event simulation Monte Carlo or stochastic simulation methods Synthetic data generation or
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the integration of both ecological and social data using platforms such as Marxan or prioritizr Experience with agent-based ecological modelling approaches (e.g., using R, NetLogo, or similar) Working
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three complementary methodologies: • Theoretical modeling (game theory and/or agent-based models) to formalize strategic interactions involving algorithmic pricing technologies. • Empirical analysis of
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, including large-scale optimization and meta-heuristics; economic paradigms (game theory, mechanism design, electronic markets); agent modeling and simulation; agent planning, scheduling and decision support
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, including large-scale optimization and meta-heuristics; economic paradigms (game theory, mechanism design, electronic markets); agent modeling and simulation; agent planning, scheduling and decision support