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application! We are looking for a highly talented and motivated Ph.D. student in communication systems, to join our research team in the cutting-edge area of AI security, on the topic of “Memory Poisoning in
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of real-time behavior, security breaches, and system-wide failures. The goal of this project is to develop a theoretical foundation for understanding and mitigating memory poisoning in LLM agents
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, including 4 training schools and two workshops. As a participant of the project, you will become part of the group ‘Adaptive In Memory Computing Group’ at PGI-14. Background MINDnet The exponential surge in
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formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science . At STIMA, we conduct research and education in both statistics and
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The Leibniz Institute for Neurobiology (LIN) is an internationally recognized neuroscientific research institute and dedicated to the research on learning and memory. Our research comprises all
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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Innovative city . The position is formally based at the Division of Statistics and Machine Learning (STIMA) within the Department of Computer and Information Science . At STIMA, we conduct research and
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reinforcement learning approaches that adapt lineup presentation in real time based on witness behaviour. A defining feature of the project is close collaboration with Promat, the leading provider of police
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device constraints Design and implement hardware-efficient training methodologies for machine learning systems Conduct comparative benchmarking and performance analysis against state-of-the-art studies
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 2 months ago
include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search