764 computer-science-intern "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Northeastern University
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, including courses in core building technology; mentoring students at undergraduate, masters, and doctoral levels; maintaining a strong program of high-impact research that attracts external funding and/or
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administrative experience, with preferred focus on residential life, program management and/or international education, is required. Robust leadership experience required, with preference for experience
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Administration Internal Number: 6821403 Part-Time Lecturer - Public Budgeting and Financial Management About the Opportunity The College of Social Sciences and Humanities and its ten tenure units are the home of
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Administration Internal Number: 6821428 Part-Time Lecturer - Institutional Leadership and the Public Manager About the Opportunity The College of Social Sciences and Humanities and its ten tenure units
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Internal Number: 6808793 Part-Time Lecturer Analytics About the Opportunity About Northeastern: Founded in 1898, Northeastern is a global research university and the recognized leader in experience-driven
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: Education & Experience: Graduate degree (Masters/PhD) in Computer Science, Engineering, Mathematics, Statistics, or similar disciplines and two or more years of professional experience, or can convincingly
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on the Boston Campus. The successful candidates will teach on-campus graduate-level courses within the Human Resources Management program, focusing on topics such as talent acquisition, organizational development
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recommendation to: J. Andrew Orr-Skirvin, PharmD, BCOP Clinical Professor, Department of Pharmacy & Health Systems Sciences Pharmaceutical Industry Fellowship Program Director 360 Huntington Ave, 140 The Fenway
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 28-Dec-25 Location: Boston, Massachusetts Type: Part-time Categories: Academic/Faculty Business/Finance Internal
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teaching interests that would further strengthen an application include machine learning, computational economics, or data-intensive methods, particularly where these areas complement macroeconomic analysis