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conditions. The research will investigate agentic AI and reinforcement learning-based control for adaptive spectrum usage, power control, scheduling, and multi-RAT coordination (integrating 5G/6G Terrestrial
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challenging. What starts here really does change the world. For more information about Texas McCombs please visit: https://www.mccombs.utexas.edu/ Purpose This position will lead talent acquisition activities
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mesoscopic properties of active units within a collective to its macroscopic behavior based on simulation data. Another possibility is to equip active units with learning capabilities or adaptive interactions
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exemplary colleagues, trusted partners, valued advisors, and agents of positive change. More about FAD: Within and across the University, we aim to be exemplary colleagues, trusted partners, valued advisors
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or noisy observations. Addressing these uncertainties typically relies on ensemble-based simulations using computationally expensive high-fidelity models. This PhD project addresses this challenge by
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discretization, and incomplete or noisy observations. Addressing these uncertainties typically relies on ensemble-based simulations using computationally expensive high-fidelity models. This PhD project addresses
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Evolution. Please check our publications for more details: http://garciajulian.com [1] “Empirical Agent Based Models of Cooperation in Public Goods Games | Proceedings of the Fourteenth ACM Conference
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23 Jun 2026 Job Information Organisation/Company DIFFER Research Field Computer science » Modelling tools Computer science » Programming Engineering » Chemical engineering Engineering » Simulation
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. Assist in deploying and managing AI-related infrastructure (e.g., virtual machines, agent-based systems). Ensure availability and reliability of systems used in teaching laboratories and workshops. 4
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the implementation of a cognitive agent embodied in a humanoid robot Unitree G1 which will collaborate with a human partner to solve a spatial problem (e.g. 3D puzzle). The tasks to be carried out are: (i) scene