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: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model
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of electrochemistry and artificial intelligence. Ideal candidates will have experience in machine learning, large language models, AI-agent development and computational workflows, with particular interest in building
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infrastructure for these studies, including prompt design as a controlled manipulation, generative voice agent pipelines, conversation logging and transcription Extending and validating existing LLM based coding
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the IDS with in-network access control and policy enforcement. The role also involves collaboration with AI researchers integrating the in-network IDS with endpoint AI detection and agentic response models
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privacy risks of AI systems used in healthcare, especially systems based on large language models, vision-language models, transformer models, and autonomous AI agents. The project may investigate attacks
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training objectives are intrinsically risky and have proposed an alternative paradigm: a non-agentic "Scientist AI" that explains the world from observations rather than acting in it, combining a world model
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infrastructure over time, under different scenarios. Expected Results: 1) Development of geometric deep learning, time-series data mining, and agent-based approaches incorporating microbial information for DWDSs
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analytics and AI experts, using modern analytical techniques to build high‑impact, agentic AI ‑based products that guide strategic decision‑making across the institution. Key responsibilities: Partner with
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analytical solutions using tools such as Qlik Sense, Power BI, Python, SQL, and Excel-based analytics tools. Contribute to the design and improvement of data models, data structures, and transformation logic
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model integrity and data confidentiality. The successful candidate will develop novel frameworks that enable effective collaboration among language model-based agents without exposing proprietary