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Postdoctoral Associate- Agelab
  • Job Number: 25481
  • Functional Area: Research - Engineering
  • Department: Center for Transportation & Logistics
  • School Area: Engineering
  • Pay Range Minimum: $71,000
  • Pay Range Maximum: $90,000
  • Employment Type: Full-Time
  • Employment Category: Exempt
  • Visa Sponsorship Available: Yes
  • Schedule:
  • Pay Grade: No Grade


Posting Description

Postdoctoral Research Associate, MIT AgeLab and MIT Center for Transportation & Logistics, for a one-year appointment, renewable based on performance and funding. The position supports engineering-driven research on real-world driving behavior, automation, and safety using large-scale, multimodal data. Will work with extensive naturalistic driving datasets, simulation platforms, and traffic models to develop methods that infer, model, and predict driver state, behavior, and interaction with advanced vehicle systems and autonomous driving. The role involves close collaboration with senior researchers to design studies, develop scalable data extraction and analysis methods, and build models that connect sensing, behavior, and system performance. Postdocs are expected to lead and contribute to peer-reviewed publications, present findings at technical conferences, engage with research partners, and help define new research directions at the intersection of human behavior and intelligent systems.

Job Requirements

REQUIRED: PhD in Industrial Engineering, Computer Engineering, Computer Science, Human Factors, Psychology, Data Science, Public Health, or a closely related field with a strong quantitative or engineering focus; demonstrated ability to conduct independent, original research, including formulating research questions, designing analyses, and executing studies using complex datasets; strong analytical and quantitative skills, with proficiency in one or more programming or statistical environments such as Python, R, or MATLAB; and experience working with big data, complex, or time-series datasets, including data cleaning, feature extraction, and exploratory analysis; ability to communicate technical results clearly in written and oral form to interdisciplinary research teams and external partners. PREFERRED: Experience with machine learning or AI methods applied to real-world data; familiarity with driving and traffic simulation environments, experimental platforms, or model-based evaluation methods relevant to transportation or human-machine systems; and experience collaborating on interdisciplinary research teams and contributing to peer-reviewed publications.

2/11/2026