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Field
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into Planning Support Systems (PSS) — the tools and models planners and stakeholders use to co-design urban futures. Your work will develop, test and critically evaluate AI components that support scenario
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, human-computer interaction, psychology, and sociology. We conduct both fundamental and applied research, striving to contribute to today's major issues. For example, we study how we can improve
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-computer interaction, or related field. Interest and experience with AI/ML concepts and demonstrated ability to learn technical tools quickly. Experience or interest in urban planning and climate adaptation
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Professor who dares to push boundaries—an ambitious leader with expertise in applying machine learning and artificial intelligence to unlock the complexity of biological systems. Your colleagues: At MaCSBio
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Cognition is highly multidisciplinary and combines communication and cognitive sciences with artificial intelligence (AI) and (computational) linguistics, human-computer interaction, psychology, and sociology
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. Main areas of interest are source coding, channel coding, multi-user information theory, security, and machine learning. We typically use information-theoretical frameworks to model the scenarios under
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infrastructure, including facility blueprints and model factories. Proficiency in the integration of automation technologies, including robotics, real-time data analytics, artificial intelligence/machine learning
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multimodal machine learning approaches that remain robust while engaging with practical concerns of privacy, ethics, and user agency. Throughout this project, you will contribute to data collection experiments
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integrate and advance behavioural insights for effective climate policy design, communication and compliance concerning climate change mitigation and adaptation through (computational) modelling and empirical
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are looking for applicants who have or expect to receive a PhD degree before joining the project. Applications should have a relevant track record in multimodal machine learning, preferably in the context