Postdoctoral Associate, Low Income Firms Transformation (LIFT) Lab
Job Description
POSTDOCTORAL ASSOCIATE, LOW INCOME FIRMS TRANSFORMATION (LIFT) LAB, Center for Transportation & Logistics (CTL), to assist LIFT Lab in research projects and teaching activities; conduct independent research under the supervision of senior researchers; conduct empirical research with micro and small firms in Latin America, including data collection, field interventions and data analysis; publish impactful research in leading academic and business-oriented journals in the field of supply chain management, with emphasis on empirical research and data analytics; work in collaboration with research staff in the development and scoping of sponsoring projects with corporate partners, including writing grants or research proposals; and serve as mentor and advisor for thesis or capstone projects for master’s program.
Job Requirements:REQUIRED: PhD in supply chain management, logistics, transportation, operations management, industrial engineering, operations research, computer science, information systems, business management, or related field; a minimum of two years of experience; in-depth knowledge and experience in one or more of the following research areas: empirical research, data analytics, statistical analysis, probability, machine learning or artificial intelligence; record of publications in peer-review academic journals in the field, or strong pipeline of potential publications; proficiency in coding in programming languages, preferably Python; excellent communication and presentation skills; enthusiasm for research, teaching and academic activities; passion for LIFT Lab and Center's broader mission; and initiative with superb work ethic. PREFERRED: in-depth knowledge and relevant experience on applied projects involving data analytics and machine learning for the context of small firms, supply chain, or related field; and teaching experience (e.g. teaching assistant, lead lecturer) for courses on supply chain management, logistics, and quantitative-oriented courses, such as coding, statistics, probability, and mathematical modeling. Job #24524
This is a temporary, one-year position with the possibility of extension, contingent upon performance and available funding.
10/18/2024
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