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Language Processing and Large Language Models, Distributed and Collective Artificial Intelligence, Reinforcement Learning and Multi-agent Reinforcement Learning or alternatively, from candidates innovating with AI in
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recover quickly from disruptions. The research will involve reinforcement learning, predictive modeling, and real-time adaptive control to dynamically optimize production sequencing, resource allocation
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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the following ones. Exploration of active auditing techniques for large machine learning models, use of reinforcement learning, potential application to recommender systems. The PhD will mainly investigate
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on other aspects of the mechanical and multiphysics behavior of heterogeneous materials. Where to apply Website https://www.academictransfer.com/en/jobs/356990/phd-on-ml-accelerated-simulatio… Requirements
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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Your Job: We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop
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. Please do not contact us for unsolicited services. Where to apply Website https://www.academictransfer.com/en/jobs/357285/postdoc-reinforcement-learning-… Requirements Additional Information Website
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how a novel machine learning-based methodology leveraging reinforcement learning with human feedback and multi-objective optimisation can be realized to generate new and even improve existing work plans
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disorders can be better recognised and treated more efficiently. About the role We are looking for a Research Fellow to join the RELMED project aiming to understand which computational (reinforcement learning