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with expertise in the following four areas: (1) working with large-scale digital trace data; (2) building and running natural language processing and machine learning workflows; (3) experimental design
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or other related department to apply. The successful applicants will design controllers for a variety of
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natural language processing and machine learning workflows; (3) experimental design and causal inference (including virtual lab experiments); and/or (4) network or computational modeling. The ideal
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. The successful applicant will contribute to four main research thrusts that are central to the success of this multidisciplinary project: (i) designing and developing chemical and ion-exchange processes for energy
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on representing the structural response Physical experimental testing for structural and geotechnical applications Data acquisition and processing from monitoring systems Validation of modeling results against
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, the candidate will develop polymeric lubricious coatings for catheters to minimize injury to blood vessels during the trans-catheterization process. They will characterize the surface modifications of implants
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, artificial intelligence, computer vision, robotics, UAVs, etc. is a plus. Other preferred qualifications include: expertise in programming and coding (preferably using Python and C++) and GUI development
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well as familiarity with machine learning workflows, natural language processing (NLP), and text-as-data methods. We are especially interested in applicants who demonstrate a strong substantive interest in using
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-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI systems, understand the design and development decisions that propagate social biases, and