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) Neuromodulation approaches (TMS, tDCS, TUS) Neurogenetics Computational modelling (machine learning, reinforcement learning) Our research bridges scales (local circuits to global networks) and species (humans, mice
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Fellowship in Reinforcement Learning and Autonomous Laboratory Systems Department Research | Tang | Michael Smith Laboratories
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 16 days ago
making problem under uncertainty. Most of our activities are related to either bandit problems, or reinforcement learning problems. Through collaborations, we are working on their application in various
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 days ago
latent information concerning the physical properties of the manipulated objects (e.g. fragility) that are usually non included in packaging methods. In addition, reinforcement learning will be used
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testing code, and learning from feedback. 🔍 Research Objectives Design an Agentic SWE Framework Model an AI system that combines reasoning, planning, and self-correction for software engineering tasks
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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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the use of reinforcement learning approaches to enable tractable active auditing, by both relaxing guarantees and by adding work assumptions for proposing efficient algorithms. Where to apply Website https
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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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, reinforcement learning, and other AI-based protein design pipelines to generate protein assemblies that guide the formation of inorganic materials, including semiconductors, magnetic materials, and other