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Financial Mathematics; minors in Computer Science and Mathematics, and an undergraduate minor and a graduate certificate program (with the Department of Psychology) in Data Visualization and Analysis
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(Lead) to work in Miami, Florida. The Clinical Informaticist 3 (Lead) leads complex projects, manages application enhancements, and ensures comprehensive support and training. This role requires a deeper
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to their ongoing research programme, which aims to unravel the complex mechanisms underpinning 3-dimensional growth in plants. About you The successful applicant will hold a relevant PhD, together with experience in
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engineering projects. Provide guidance to project managers on proposal analysis/proposal development, workload planning, project schedules, and cost/budget to ensure projects align with program capabilities and
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systems, computational mathematics, data science, difference equations, financial mathematics, mathematical biology and modeling, numerical analysis, numerical linear algebra, applied probability/statistics
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analysis, enrolment planning, higher education funding, and public policy within a complex and collaborative environment. The successful candidate will engage with a broad range of stakeholders and translate
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods
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meritorious if you provide transcripts with documented coursework in algorithms, theory of computing, quantum computing, and discrete mathematics. provide published work on the design or analysis of modes
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identification, gene cluster family classification, and functional prediction, to work with highly fragmented and complex eDNA datasets. This project is part of the larger European AEGIS consortium, bringing
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that support institutional planning and decision-making at NC State. You will apply statistical methods, predictive modeling, artificial intelligence, and machine learning to analyze complex university data and