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Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) | Leipzig, Sachsen | Germany | 2 months ago
representations to support cognition across domains. Under this umbrella, we explore: Spatial navigation, temporal, episodic, and working memory Learning, decision making, and knowledge acquisition Translational
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Type Staff Job Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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, machine learning, governance and operations, we aim to address the complex challenges and opportunities of space exploration. CfAI is a large research group in the Department of Physics at Durham University
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are seeking a highly motivated researcher with a strong background and interest in machine learning and artificial intelligence. The position focuses on the development and application of advanced deep learning
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-scale, diverse datasets using machine learning and AI techniques. Findings will be disseminated through peer-reviewed publications, conference presentations, and public engagement. About You You will hold
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for reasoning, on efficient and explainable machine learning for extracting and structuring information from large datasets, and on combining the two in neuro-symbolic AI.
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promotes a dynamic learning environment in which qualified individuals of differing perspectives, life experiences, and cultural backgrounds pursue academic goals with mutual respect and shared inquiry. EEO
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against