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models (LLMs) have become significantly more difficult to detect, as a poisoned parameter update is harder to recognise than a poisoned dataset. Therefore, several methods have been developed recently to
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pipelines by combining statistical, synthetic, and decentralized methods to improve both the privacy and utility of anonymized datasets. Moreover, this research will examine the potential of AI and LLM
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description 1- Evaluating LLM Use for Mental
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domain-specific terms. Today’s large-scale language models (LLMs) such as Llama-3 (Meta, 2024) can be fine-tuned using limited data (even with a few instances) for domain adaptation, making them a valuable
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(LLM) and other key agentic tools (starting with MCP or LangChain), the project will design, specify, implement, and evaluate advanced capabilities such as self-organisation, spontaneous generation, self
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frameworks (PyTorch, TensorFlow). Experience with dataset curation, annotation workflows, FAISS/embedding retrieval, LLM-based parsing, RAG-style pipeline, and GPU/HPC training. Familiarity with 3D data
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medicine and diagnostics, epidemiology and biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will
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their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? We are looking for a recognised business development
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AI-enhanced solutions to address specific pedagogical needs. Investigate how learners engage with technology and generative AI such as LLMs, and develop targeted tools and learning interventions
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. You will develop dynamic models and apply them, for example, to analyze sociotechnological networks and to model interactions between humans and AI agents (such as LLM-based chatbots and autonomous