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-focused programs that allow students to get ahead without leaving their community. Learn more about CIP at https://www.boisestate.edu/ruraleducation/ . CIP is part of the Interdisciplinary Professional
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optimize experimental protocols, analyze complex datasets, and contribute to scholarly publications. Emphasis is placed on artificial intelligence/machine learning approaches applied to digital data and
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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técnicas de machine learning e IA. Sólidos conocimientos en análisis y tratamiento de datos, programación y desarrollo de modelos analíticos. Experiencia con bases de datos y herramientas de análisis
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LevelMaster Degree or equivalent Skills/Qualifications Solid background in Machine Learning and Deep Learning. Experience or interest in agentic AI frameworks (e.g., LangChain, LangGraph, AutoGen, or similar
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AI and data science, particularly in dynamic settings where observations are collected sequentially and decisions influence future outcomes. This project will develop novel machine learning and
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Assistant/Associate Professor of Practice in Human-AI Interaction, Machine Learning, and Data Design
developing, training, and deploying advanced AI, data, and machine learning systems from a human-centered innovation lens. Preference will be given to candidates whose background includes i) experience
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of relevant research experience. Background conducting quantitative research in healthcare. Technical Skills or Knowledge: Proficiency in optimization, statistics, machine learning, econometrics, or AI
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machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in industrial furnaces
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, calorimetry, and synchrotron experimental measurement techniques. Knowledge of AI-based and machine-learning methods is also beneficial. For further information about a specific subject see General syllabus