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are looking for a researcher with a PhD (or near completion) in engineering, data science, computational social science or a related discipline, with experience in data analytics, NLP or machine learning. You
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Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs. The candidate will apply their expertise to advance predictive maintenance systems using AI
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with Prof. Aditya Vashistha and collaborate with faculty and students participating in the Cornell Global AI Initiative Qualifications Applicants should have: A PhD in HCI, AI, NLP, Information Science
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language processing (NLP), and ontology-based frameworks to enhance simulation, curriculum development, and personalized learning in health professions education Develop and evaluate AI-powered tools such as chatbots
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foundations of agentic reasoning, in close dialogue with complex real-world problems. Essential requirements: PhD (or near completion) in AI/ML/NLP, Computer Science, or related * Strong research track record
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modeling, LLM and/or NLP, behavioral coding, and/or psychophysiological monitoring. For consideration, please click the link below to apply and submit all required application materials: https
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or NLP for classification, prediction, or multimodal data processing Experience with annotation tools, data pipelines, or AI-assisted labeling workflows Strong research communication skills and a record of
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politics, using, among others: Econometrics, field and survey experiments, and quasi-experimental causal inference methods, Natural language processing (NLP), and Comparative case studies As part of the ERC
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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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, including prompting, fine-tuning, or evaluation Machine learning or NLP for classification, prediction, or multimodal data processing Experience with annotation tools, data pipelines, or AI-assisted labeling