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Job Description We are seeking a highly motivated Postdoctoral Associate - Community-Centered Physical AI for Smart Urban Management to contribute to a 1-year research agenda on community-centered
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administrative data - Experience with process mapping and/or process improvement - Experience building solutions within policy, legal, and contractual requirements as part of a larger team Overtime Status Exempt
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. The candidate will apply a suite of statistical and physical models for integrating observations of different accuracies, improving predictions of future hazards. The work will further include research, writing
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of agentic AI systems for accelerating computational catalysis and experimental design. The successful candidate will contribute to building AI-native frameworks that combine physics-based modeling, machine
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collaborators. Required Qualifications • PhD in neuroscience, physics, computer science, applied mathematics, biomedical engineering, or a related quantitative field. PhD awarded no more than four years prior
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systems. • Develop and implement predictive models for packaging performance using machine learning approaches and physics-based simulations. • Investigate logistics optimization strategies for packaging
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· Ph.D. degree in Computer Science, Electrical and Computer Engineering, Computational Biology, Bioinformatics, Statistics, Mathematics, Biophysics, Physics, Chemistry, Biology or related fields. · PhD
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applying these tools to catalytic reaction engineering and methane conversion chemistry. Required Qualifications - Ph.D. in Chemical Engineering, Chemistry, Materials Science, Physics, or a closely related
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in vivo neurophysiological recordings and behavioral assays in mice and tree shrews. • Data Analysis: Process large-scale neural datasets, applying computational tools to model behavioral and neural
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have the chance to work with top-notched researchers from both academia and industry on future wireless systems spanning from novel physical layer waveforms designs (e.g., MIMO, OTFS) to online real-time