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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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in R, MATLAB, Python and/or other programming languages. Experience in AI and machine learning techniques applies to physiological, neural, and imaging data. Preferred qualifications Experience with
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equitable scholarly environment in research, mentoring, and service. Your work will focus on the SEAMLESS (SEmi-Automated Machine LEarning Search for Semi-resolved galaxies) survey, whose goal is to identify
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computer science programs (Chemical Engineering, Civil and Environmental Engineering, Computer Science, Electrical and Computer Engineering, and Mechanical and Industrial Engineering). This two-year
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC