352 learning "https:" "https:" "https:" "https:" "https:" "https:" "Inria" positions at University of New Mexico
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Learning Specialist Requisition ID req37654 Working Title Learning Strategist Position Grade 12 Position Summary Manage caseload of student athletes with documented learning exceptionalities or
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Student Intermediate Level Requisition ID req37082 Working Title STEM Learning Center Pay $15.00 Hourly Campus Taos Department Taos Branch Employment Type Student Employment Student Type Work-Study
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Training & Devt Specialist Requisition ID req37767 Working Title Training & Development Specialist - SOM Assessment & Learning Office Position Grade 11 Position Summary Are you an experienced adult
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component of the University of New Mexico School of Law’s Law and Indigenous Peoples Program (LIPP; https://lawschool.unm.edu/indian/index.html ). LIPP integrates academic study, experiential learning
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pursuing research in one or more areas central to modern Data Science, such as statistical learning, causal inference, experimental design, probability, stochastic PDEs, algebraic statistics, normalizing
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Research Scientist 3 Requisition ID req38172 Working Title Research Scientist 3 Position Grade 13 Position Summary The Webster Lab (https://websterlab.weebly.com/) and Litvak Lab (https
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Coord,Special Events Requisition ID req38137 Working Title Special Events Coordinator - SOM Office of Continuous Professional Learning Position Grade 08 Position Summary Do you like to help with
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, Ellis alums can be found having an impact in their communities all over the world. Ellis emphasizes collaborative student interaction, active adult learning, team experiences, and scholarship that draw
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communities all over the world. Ellis emphasizes collaborative student interaction, active adult learning, team experiences, and scholarship that draw upon a variety of methodologies and subject areas. New
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central to modern Data Science, such as statistical learning, causal inference, experimental design, probability, stochastic PDEs, algebraic statistics, normalizing flows, scientific machine learning