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Lahore University of Management Sciences

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Research Associate in AI Hub for Maternal, Newborn, and Child Health (MNCH) Lahore University of Management Sciences in

Degree Level

not provided

Field of study

Computer Science

Funding

Full funding available

Deadline

December 31, 2026
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Country

Lahore University of Management Sciences

University

Lahore University of Management Sciences

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Keywords

Computer Science
Medical Science
Salud Pública
Maternal And Child Health
Econometric
Economics
Statistics
Newborn Care
Machine learning

About this position

We are hiring a Research Associate for the AI Hub for Maternal, Newborn, and Child Health (MNCH) at Lahore University of Management Sciences (LUMS), Pakistan. This is a data-driven research role focused on maternal and child health outcomes, with work spanning large health and survey datasets, multi-source data integration, and quantitative analysis.

The position is especially relevant for candidates with interests in Public Health, Economics, Statistics, Computer Science, and Medical Science. The role involves cleaning and managing datasets, applying econometric and machine learning techniques, mapping maternal and child health pathways and stakeholder interactions, and supporting the selection and implementation of estimation strategies through engagement with relevant literature and comparable studies.

Eligibility highlights: a Master's degree in Economics, Public Health, Data Science, or a related field; strong quantitative and analytical skills; proficiency in Stata (required); familiarity with R or Python is a plus; experience with data cleaning, multi-source data integration, descriptive analysis, regression-based approaches, classification, prediction methods, and machine learning; and the ability to work independently, take initiative, and meet deadlines.

Funding/compensation: this is a paid research position with a monthly salary of PKR 150,000–200,000, commensurate with experience. The appointment is for approximately 1 year, with a possibility of extension.

Application process: shortlisted candidates will be evaluated through an interview, submission of existing research or analytical work, and a practical exercise to assess technical skills. Applicants should email their CV, a brief statement of interest, and a sample of research or analytical work (such as papers, data analysis projects, or code samples) to [email protected].

Deadline: preferred deadline is 2025-04-16.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

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