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. The interests of LABS are to develop and apply statistical, machine learning, and artificial intelligence (AI/ML) methodologies to "big data" in multi-omics and medical data for aging and diseases, such as
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, implementation, and analysis of machine learning models for computer vision tasks (40%). Analysis of natural scene statistics in aquatic and terrestrial environments (40%). Design of models to learn texture
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organization, computer literacy and communication skills. Able to manage multiple tasks simultaneously. Fluency in Spanish beneficial; Additional languages an added plus. Computer proficiency in Microsoft Office
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. Responsibilities Washes dishes, pots, and pans. Shampoo rugs. Cleans and washes floors and walls. Uses equipment such as, but not limited to, dish washing machines, rug shampoo machines, and pot washing machines
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for the support, maintenance, user training, operation, documentation, and security of AV/IT systems located in campus-wide learning environments, conference rooms, and specialty rooms for the Columbia University
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and CAM programming. Supporting faculty in the development and implementation of laboratory activities and design-based courses. Training students in the safe use of machine tools, electronics equipment
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Coordinator is responsible for maintaining project databases and Computer Assisted Personal Interview (CAPI) software, preparing datasets for project studies, undertaking data analysis and preparing reports
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. Responsibilities Analysis of activations maps in mouse brains, primarily using R free-source library. Develop computer codes, primarily using ImageJ and Python, to implement algorithms for characterizing brain maps
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: Prior experience running behavioral experiments is desirable, as is previous collaboration or engagement with researchers in economics. Familiarity with methods from machine learning will be a plus. All
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relational database environments Apply and evaluate methods from causal inference (e.g., confounding control, bias assessment, sensitivity analyses) Apply machine learning approaches for predictive modeling