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intelligent autonomous systems operating under uncertainty, limited information, strategic human behavior, and complex multi-agent interactions. The postdoc will work at the intersection of control theory
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per year, if mutually desired. Research will focus on multi-level investigation of safety-critical human-in-the-loop systems that collaborate with automated/autonomous decision-aid technologies. Desired
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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The focus of the project is artificial intelligence (AI) and its relation to robotics and embodiment. Embodiment plays a significant role in learning in AI by enabling cognitive agents to acquire actively
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multi-agent pathfinding (MAPF) algorithms - Experience across multiple areas is a strong plus; Experience developing ML-based optimization approaches is a strong plus; A strong publication track record is
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
part of the DOE–BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC) , a multi-facility collaboration integrating experimental modalities and simulations to enable an orchestrating
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Perturb-seq, with our integrative analyses often employing computational approaches such as agent-based modeling to deconstruct gene-regulatory networks and predict system behaviors. As a postdoctoral
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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(PyTorch, Tensorflow etc.) Knowledge of multi-agent systems and autonomous agent modelling Expertise in Machine Learning and Artificial Intelligence We consider the following as an advantage: Willingness
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particular, there is a need for a better understanding of how embodied cognitive agents can learn to solve complex problems and adapt in dynamic and challenging real-world scenarios such as underwater