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related field in hand by the time of the appointment. Strong background in distributed control, optimization, or multi-agent systems. Proven track record of high-quality publications. Proficiency in
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in hand by the time of the appointment. Strong background in distributed control, optimization, or multi-agent systems. Proven track record of high-quality publications. Proficiency in programming (e.g
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related field in hand by the time of the appointment. Strong background in distributed control, optimization, or multi-agent systems. Proven track record of high-quality publications. Proficiency in
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writing C++ and PyTorch. Training and debugging RL agents. Imitation Learning algorithms for robotics or autonomous vehicles. Prior work combining RL with human data or feedback. A track record of code
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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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data of emotional states and personality metrics. Modeling efforts will focus on agent-based approaches, which may include both simulation studies and data-driven modeling approaches to understand
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research. The Postdoctoral Scholar – Pharmacometrics in the PhASR will support data collection, quality control of data, data analysis, modeling, simulation and PK/PD study design for human clinical trials
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stress contagion and collective motion, as well as survey data of emotional states and personality metrics. Modeling efforts will focus on agent-based approaches, which may include both simulation studies
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: Expert on steels and steel welding or additive manufacturing Develop advanced machine learning framework to combine different modality and fields of data Conduct CALPHAD-based simulations in a high
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University of Arizona working in the area of reinforcement learning, multi-agent systems, neuro-symbolic AI, and trustworthy intelligence. We invite qualified candidates to join our group and participate in