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Field
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innovative material solutions. This project tackles this challenge head-on. The project vision is to develop a pioneering AI-driven methodology for designing Functionally Graded Materials (FGMs) specifically
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of the project is to develop new conceptualizations of what AI means to people, grounded in everyday life, and to assess democratic implications of emergent media technologies. The postdoc will design and conduct
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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Fellow in Autonomous Systems and Control to design and implement efficient, performance‑guaranteed distributed control approaches, leveraging cutting‑edge learning algorithms and AI strategies
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intelligence (AI), and analyses changes in trust and understandings, at a time when AI becomes integrated into everyday media experiences. The goal of the project is to develop new conceptualizations of what AI
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learning, and data science, with a particular focus on neuroscience applications. Designs AI techniques and algorithms for multimodal data fusion (e.g., MRI, EEG, cognitive and behavioral data, blood
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assessment”. They will be required to: (a) conduct advanced research on multi-modal fusion techniques for localization and mapping; (b) develop and implement algorithms for autonomous robots; (c
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techniques for localization and mapping; (b) develop and implement algorithms for autonomous robots; (c) design and execute experiments to validate and refine algorithms and systems; (d) publish
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, operations research, and management science, developing rigorous and practical methods for algorithmic risk assessment. You will become part of the broader AI2 collaboration, involving leading researchers from
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. In this role, you will be part of the research team, working to develop and evaluate privacy-preserved Generative AI algorithms for generating synthetic Personal Identity Information (PII). This aims