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Massachusetts Institute of Technology Digital Learning Postdoctoral Associate in Cambridge, Massachusetts

Digital Learning Postdoctoral Associate

  • Job Number: 23755

  • Functional Area: Research - Engineering

  • Department: Center for Transportation & Logistics

  • School Area: Engineering

  • Employment Type: Full-Time

  • Employment Category: Exempt

  • Visa Sponsorship Available: Yes

  • Schedule:

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    Job Description

DIGITAL LEARNING POSTDOCTORAL ASSOCIATE, Center for Transportation & Logistics (CTL), to serve as CTL’s research liaison to MITx in terms of innovative educational techniques, best practices across other massive open online courses (MOOCs), improvements to ensuring academic integrity, etc. Will conduct research in data analytics and digital learning at a massive scale; recommend, develop, and apply visualization methods, statistical models, and descriptive analytics techniques to better understand learner behavior across a multi-MOOC-based program in supply chain management (SCM); recommend, develop, and apply machine learning models and other techniques to better categorize learners and build predictive behavior models; implement experiments testing the impact and effectiveness of potential prescriptive interventions to increase the activity and performance of learners; submit and publish papers on descriptive, predictive, and prescriptive analyses in online education; perform data-driven analysis; ensure the development, implementation, and evaluation of new digital tools/assessments; and assist with running MOOCs in SCM.

Job Requirements

REQUIRED: Ph.D. in data science, learning analytics, learning sciences, operations research, information systems, computer science, engineering, or related discipline that includes domain-specific knowledge of data analytics, statistics, and educational data mining; STEM teaching experience in higher education settings and proficiency evaluating the effectiveness of learning materials; strong problem-solving, interpersonal, and communication skills; working knowledge of/familiarity with statistical software and analytics tools to analyze big data sets; and ability to support multiple projects simultaneously in a fast-paced environment, work independently and collaboratively, and build strong working relationships with teammates/faculty/staff. PREFERRED: SCM background; data analytics skills; proficiency with BigQuery and Python; and interest in educational technology, digital teaching and learning in higher education, producing educational content, and delivering and managing online educational programs. Job #237552/20/24