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algorithms to shape the liveable cities of tomorrow? Job description Human-centred AI techniques, such as Reinforcement Learning from Human Feedback (RLHF), hold great potential for supporting design
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. The successful applicant will be expected to design and implement analytical and interpretability frameworks to investigate the internal representations and decision-making processes of machine learning models
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. The successful applicant will be expected to design and implement analytical and interpretability frameworks to investigate the internal representations and decision-making processes of machine learning models
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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POST: Grade 7 Postdoctoral Researcher (maternity cover 6 months minimum) INFUZE project Intelligent Mobility Design Centre, RCA Inspiring Futures for Zero Carbon Mobility – Postdoctoral Design
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
, and real-time classroom monitoring. Design and implement machine learning models to analyze multimodal data (e.g., student behavior, engagement, and performance) to enhance personalized learning
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, disability studies, or inclusive design Experience with participatory design or community-engaged research Technical skills in prototyping, data analysis, or machine learning are a plus Experience with human
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change via reform efforts inspired by Science Education Research and/or the Learning Sciences. The Post Doctoral Scholar will be expected to contribute to the design of qualitative (e.g., semi-structured
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Science, Learning Sciences, or a closely related field Experience with game design, game development, or creative computing tools and platforms Demonstrated interest in youth-centered or community-engaged research
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Academy of Engineering Green Future Fellowship, the postholder will design and integrate automated experimental workflows and data pipelines to accelerate the discovery and optimisation of OPV devices