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Field
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-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs, including prompting, fine-tuning, or evaluation Machine learning
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efficiency of large-scale AI systems, such as foundation models (e.g., LLMs, VLMs, Multimodal models) and World models, while maintaining or improving performance. Design and implement next-generation
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learning Broad familiarity with geospatial programming libraries Preferred Knowledge, Skills, and Abilities: Non-LLM foundation model expertise Time Series Foundation Models Expertise with Graph transformers
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and health. The lab conducts method-driven research in natural language processing (NLP) and artificial intelligence (AI), with an emphasis on LLM reasoning, uncertainty quantification, interpretability
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. Candidates should have a strong research record in LLM-based agents, reinforcement learning, or large language models, preferably in areas closely aligned with the topics outlined above. Desired: We are
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prior experience doing so is not required. Additional beneficial but optional experience and skills include multi-level modeling, LLM and/or NLP, behavioral coding, and/or psychophysiological monitoring
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ICT Services & Applications. Your role We offer an attractive research associate position in the context of the project Metamorphic Security Testing Assisted by LLMs (MORTAL), which is supported by
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Large Language Models (LLMs). The position will also involve creating quantitative evaluation frameworks to assess the quality, realism, and reliability of generated data, as well as integrating graph
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models (e.g., LLMs, multimodal systems) that support learning, assessment, and teaching practice. Empirical Education Research – Conduct controlled experimental studies (e.g. mixed-methods), including
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following areas: foundation models, multimodal learning, LLMs, AI agents, or trustworthy AI. Proficiency in Python and PyTorch Responsibilities Research Conduct independent and collaborative research in