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National Institute for Materials Science (NIMS) | JP Nagar III Phase, Karnataka | India | 3 months ago
science data, including scientific papers, experimental data/images, and crystal structures. Specifically, the candidate will be responsible for the continuous pre-training and fine-tuning of LLMs using our
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Keystone project on Addressing socio-technical limitations of Large Language Models (LLMs), particularly for medical and social computing https://adsolve.github.io/ ). The current role involves augmenting
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field evaluation connect; a minimum offline/shared-device architecture and data flow; the role and limits of AI, including one capability that should not use an LLM; safeguarding, consent/assent, referral
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analysis, or assistive technology; Experience in applying AI methods such as machine learning, deep learning, computer vision, multimodal data analysis and large language models (LLM) in health-related
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degradation, which require collaborative cross-border approaches. This Post-doctoral Research Fellow Level 1 post is funded by the Climate+ Co-Centre (https://www.climatecocentre.org/ ). The successful
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accelerators. The project will address the following key areas: Transformer-based models, including Large Language Models (LLMs), Vision Transformers (ViTs), and Vision-Language Models (VLMs), are now central to
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details of the grant can be found here: https://www.ssrc.edu.sg/projects/thematic-grant/lorenz2025/ ) (BIZ) Under Dr Lee Kwok Hao’s startup grant, the co-PI(s) plan to examine emerging issues
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. Design methods for security analysis and verification of shared data and models. Develop tools for data processing, LLM training and security evaluation. Write research papers. Other duties as assigned
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AI for cultural heritage within the NARRATIA project. Activities will include Knowledge Graph-Based Narratives, RAG/GraphRAG integration with LLMs, personalised narrative generation, provenance
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of foundational and agentic AI models (LLMs, generative AI, RAG-based architectures) for autonomous, context-aware decision-making. Researching and implementing trustworthy human-machine interaction methods