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international environments. You communicate clearly in English and can represent your work in scientific and technical communities. You have an interest in working with industry stakeholders and translating
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of/on networks with higher-order interactions. This will be in collaboration with an NSF-funded ASU-UCLA team using ants as a model system and developing theory to characterize dynamic processes on networks as
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is the official language of the University, and the university research community is fully international, with more than 50 countries represented. OIST is rapidly gaining recognition in the worldwide
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applicant should have: A strong mathematical background, particularly in probability theory and stochastic processes. Familiarity with stochastic differential equations. Ideal Qualifications: Previous
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particularly diverse and open professional community, benefiting from welcome and inclusion schemes, training and internal mobility. It means participating in an academic, scientific and human adventure. It
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, and departmental seminars, and will have opportunities to mentor undergraduate and graduate students, support new group members, and collaborate with colleagues across the Duke research community
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, or a related area. Strong background in statistical theory for high-dimensional data. Computational skills in R and/or Python (ability to develop user-friendly codes). Strong written and oral communication
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: Beijing, Beijing 100190, China [map ] Subject Areas: Physics / Plasma Physics , Nuclear Experiment (nucl-ex) , Nuclear Physics , Nuclear Theory , Nuclear Theory (nucl-th) Appl Deadline: (posted 2026/01/04
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perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems This position
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prior experience conducting social science experiments (lab, lab-in-the-field, online, and/or field). Researchers working at the intersection of experiments and other methods (theory, empirical) as well