Job Description

POSTDOCTORAL ASSOCIATE, MIT LOW INCOME FIRMS TRANSFORMATION (LIFT), Center for Transportation and Logistics (CTL), to assist with research projects and teaching activities, conduct independent research under the supervision of senior researchers, and perform other duties as part of the MIT Low Income Firms Transformation (LIFT) Lab. CTL conducts research, outreach, and educational activities with a number of different companies, organizations, and governments. In order to expand the scope of its center in Latin America and increase its presence to solve the region's urgent challenges, CTL has established LIFT, a new research lab aimed at alleviating poverty and LIFTing the life of the bottom billion. Its approach is to contribute to the survival and growth of micro and small companies by improving their business and supply chain management capabilities.

Job Requirements

REQUIRED: Ph.D. in supply chain management, logistics, transportation, operations management, industrial engineering, operations research, computer science, information systems, business management, or related field; in-depth knowledge of and experience in at least one of the following: empirical research, data analytics, statistical analysis and probability, and machine learning/artificial intelligence; track record publishing in peer-reviewed academic journals in the field and/or pipeline of potential publications; coding proficiency, preferably with Python; excellent communication and presentation skills; self-motivation; and an interest in research, teaching, and other academic activities and in contributing to the success of LIFT and CTL's broader mission. PREFERRED: in-depth knowledge of and relevant experience working on applied projects involving data analytics and machine learning in the context of small firms, supply chains, and/or related fields; and teaching experience (e.g., teaching assistant, lead lecturer) in supply chain management, logistics, and quantitative-oriented courses (e.g., coding, statistics, probability, mathematical modeling). Job #22823

This is a temporary, one-year position with the possibility of extension, contingent upon performance and available funding.

6/5/23