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20th September 2026 Languages English English English The Department of Materials Science and Engieering has a vacancy for a PhD Candidate in large language models(LLMs) for data extraction and
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, persistent, and safe. Large language models (LLMs) are often adapted after training through fine-tuning, knowledge editing, and activation steering. While efficient, their effects are fragile. Interventions
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LLM program marketing. Help create and disseminate monthly newsletters, digital and physical brochures, blogs, and articles using digital marketing software to create relevant marketing materials and
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the ACK project. Design, development and validation of RAG-LLM components. Validation of cognitive state in the RAG assistant. Validation of hybrid human-automated assessment. Apoyo en la investigación para
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Scalia Law School is a Top 50 law school. A relatively young law school, Scalia Law offers students an opportunity to pursue a JD, JM, or an LLM. About the Position: The Assistant Director of JD Admissions
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evaluate large language models (LLMs) assisted methods for extracting, organising and assessing complex scientific information related to materials. The initial application will be phase diagrams and related
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here: https://itacademy.harvard.edu/ . Job Description Harvard Library and Harvard University IT are advancing the Reimagining Discovery initiative to transform how researchers, students, and the global
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Computational pathology and digital pathology using whole slide imaging (WSI) Spatial biology, spatial transcriptomics, and multi-omics data integration Large language models (LLMs) and foundation models
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, supported by Large Language Models (LLMs). Mandatory requirements: The candidate must hold a Ph.D. in Computer Science or a closely related field. Desirable requirements: Experience in Information Retrieval
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
for sensitive data Experience applying LLMs to structured information extraction from unstructured text (e.g., named entity recognition, relation extraction, schema-constrained or function-calling-based