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interaction, (vi) Network Science. The ideal candidate is self-motivated and hard-working with a PhD in one of the following: Data Science, Computer Science, Computational Social Science, Information
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inequality, (iv) diversity, (v) online controlled experiments, and (vi) network science. The ideal candidate is self-motivated and hard-working with a PhD in Data Science, Computational Social Science
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and implementation of incentive mechanisms for sociotechnical and cyber-physical-human systems, with particular emphasis on smart mobility and urban transportation networks. In particular
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to existing open-source engines. Publish in top-tier conferences (SIGMOD, VLDB, PODS, ICDE, ICDT, etc.) and participate in the wider research community. Mentor students and contribute to a vibrant research
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models to analyze and mitigate fine particulate matter (PM2.5) exposure from various infrastructure systems (e.g., transportation networks, manufacturing systems, and truck routing). Assessing
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
, Unity, or IoT frameworks). Excellent written and verbal communication skills in English. The terms of employment are competitive and include housing and educational subsidies for children. Applications
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research environment. Outstanding written and verbal communication skills are essential. The terms of employment are very competitive and include housing and educational subsidies for children. Applications
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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management Cognitive radio or adaptive communication systems, including dynamic spectrum access, band selection Heterogeneous network architectures, including terrestrial and non-terrestrial networks Deep
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, speech, images, and physiological signals. Preferred Experience: The lab highly values candidates with one or more of the following experiences: Human-Centered Applications: Familiarity applying ML in