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ultimately shifts in species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species
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more sustainable lifestyles when it comes to more plant-rich diets. We use an innovative connection of behavioural and social experimentation and agent-based models to understand and model how policy
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clinically characterized samples, and use the results to test and refine the lab’s theory of agency. The work will generate first-author papers and an agent-based research line the candidate can carry
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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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modeling Experience with machine learning, hydrologic modeling, or energy systems modeling (e.g., ParFlow, WEPPCloud, agent-based models) Proficiency in scientific programming languages (e.g., Python
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will work under the supervision of PI Calina Copos and will engage in collaborative work with cell and developmental biologists. Expertise in agent-based models, continuum PDE descriptions, dynamical
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. Familiarity with systems thinking, system dynamics, agent-based modeling, or other approaches for analyzing complex social-ecological systems. Experience conducting research in India, or more broadly in
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experience with system dynamics modelling. Experience with agent-based modelling, social return on investment analysis or other analytical frameworks to model social and systemic impacts of social policies
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empirical findings into an agent-based model and cost-effectiveness analysis and to translate study findings into a decision-support toolkit for public health agencies and policymakers. The fellow is
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contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary