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Field
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using wearable electroencephalogram (EEG) during sleep. Our goal is to pioneer developments in wearable technology that integrate reinforcement learning to discover novel strategies for human interaction
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package contains: We are seeking a highly motivated postdoctoral researcher to work on reinforcement learning methods for public health decision-making in the context of epidemic emergencies, with potential
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are seeking a highly motivated postdoctoral researcher to work on reinforcement learning methods for public health decision-making in the context of epidemic emergencies, with potential impact on public health
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) at the University of Southern Denmark invites applications for a postdoctoral research fellowship position within the field of neuro-morphic reinforcement learning to be filled earliest by 1 October 2026 for a period
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)." Successful applicants for this position will have the opportunity to work on cutting-edge research at the intersection of reinforcement learning (RL), world models, embodied intelligence, and human learning
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; expertise in several of the following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and
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Are you interested in advancing our understanding of fracture and damage tolerance in fibre reinforced polymer composites, and in contributing to the development of new modelling approaches
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/instruction tuning methods. * Understanding of Reinforcement Learning (RL) principles, multi-objective optimization, or active learning/Bayesian optimization. * Strong programming capabilities in Python
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a sequential decision-making process explored through computational simulation and deep multi-objective reinforcement learning. The project will investigate a simulation platform that reproduces
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reinforcement learning. The project will investigate a simulation platform that reproduces the structure of real prospection activities by integrating multiple, heterogeneous geospatial and archaeological data