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/ Robust) Combinatorial Optimization, Game Theory, and Network Theory, as well as Artificial Intelligence. Potentially, scenarios could be simulated using agent-based, discrete-event, or other techniques
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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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system dynamics, agent-based modeling, and discrete choice experiments. A passion for improving healthcare systems, particularly in oncology, and an understanding of patient preferences in the context
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: Optimize network design: Develop a robust, scalable thermodynamic–hydraulic framework to size and configure prosumer-based heating and cooling networks under conditions of price volatility, CO2 taxation
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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
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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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gyre (SPG) and Arctic and Antarctic sea ice cover, involving processes on smaller scales, which are often not well represented in modelling efforts focusing on the large scale tipping elements
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affect regional agriculture and transport and possibly global food security. Recently, researchers from IMAU have for the first time modelled a full AMOC collapse in a full-fledged climate model
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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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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics