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and artificial intelligence. This role offers a unique opportunity to support the development and evaluation of a multi-agent Retrieval-Augmented Generation system designed to accelerate understanding
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distributed training, experiment tracking, and MLOps automation. Problem-Solving Skills (15%) – Innovate adaptive fine-tuning, multi-task learning, and agentic reasoning strategies to improve generalization and
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 19 days ago
fields including health, agriculture and ecology, sustainable development. More information, please visit https://team.inria.fr/scool/projects Odalric-Ambrym Maillard is a permanent researcher at Inria. He
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modeling and coordination of interdependent infrastructure systems and their subsystems (such as networks of transportation, gas, electricity grid), multi-agent reinforcement learning for distributed
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, and direct patient care. This is a multi-faceted position that requires a deep understanding of infusion workflows, SOPs, general nursing practice, guidelines, and policies. Key Responsibilities: Review
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on the employee receiving approval in the FlexWork@RU Application System. Additional information may be found at https://futureofwork.rutgers.edu . Union Description Admin Assembly (MPSC) Payroll Designation
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high-level expertise to research on AI safety in multi-agent systems. The appointee will work as part of a collaborative research team to investigate open challenges in AI safety, including surveying and
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foundation models and agentic AI models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization. Experience in multi
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latency constraints. - Federated and distributed learning for RAN hardware infrastructure management. o Knowledge of autonomous AI systems based on agents. Indicative skills/experience: - Multi-Agent
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to quickly quantify the damage to forest plantations after a cyclone or a tropical storm. There is unrealised potential in using multi-modal computer vision methods that synthesis multi-source Earth