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May 2025 Apply now In human society, communication is an effective mechanism for coordinating the behaviors of humans. In the field of deep multi-agent reinforcement learning (MARL), agents can also
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study. The student would be based in the Department of Computer Science in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter. Computing systems increasingly operate in
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with established causal models. Ultimately, you will design algorithms for causality-based analysis and counterfactual recovery of liveness violations. Information and application Are you interested in
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of the PhD student based at CWI in Amsterdam will study integrated hydrogen-electricity markets. In particular techniques from Artificial Intelligence and multi-agent systems for modelling new types of markets
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progress. In this project, you will characterise the microbial, metabolic, and functional composition of FMTs used to treat patients. Your aim will be to identify the key bioactive agents within donor
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to the following: Develop a comprehensive understanding of the oxidation kinetics and mechanisms using different oxidising agents. • Develop a comprehensive understanding of the leaching kinetics and
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for blockchain-based systems. Develop mathematical and computational models for simulating and analysing various economic properties of blockchain-based systems. Design and evaluate token economies and governance
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, integrated assessment or agent-based models) and/or research on sustainability transitions and transformations will be highly regarded. Strong communication skills and the ability to work independently and as
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robust applications. • Experience in using AI tools and frameworks, particularly for the development of agent systems based on large language models (e.g., LangChain, Hugging Face, or equivalents
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 months ago
via probability distributions. Another approach is based on the use of multi-agent reinforcement learning methods of the actor-critic type. In particular, we will seek to establish convergence