• Job Description

  Req#: 65310
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  Research Assistant
  Department: Population Health
  Entity: Medical College
  Location: Nairobi, Kenya
  Introduction
  Established in 2004, the Aga Khan University Medical College in East Africa seeks to provide high quality education for health professional leaders who will then go on to become reputable specialists generating a positive and lasting impact in the lives of their patients and in the communities they serve. Our goal is to produce health professional leaders through our unique training programmes, building strong research programmes, supporting best practice in the delivery of health services, and contributing to the broader community through strategic partnerships regionally and globally. We aspire to build for the youth of East Africa, a medical college of international quality, providing them with the opportunity to get the best health professional education that is relevant and has impact across East Africa, and indeed, in the world.
  Job Summary
  The Early Prediction of Gestational Diabetes Mellitus (GDM) using Machine Learning (ML) study is part of the projects funded under the UZIMA-DS pilot project grant, aimed at crafting precise and robust ML algorithms capable of swiftly predicting GDM during the first and second trimester of pregnancy in Kenya. The objective of this study is to enhance maternal and fetal health outcomes through timely interventions. Moreover, the research outcomes will enrich understanding of GDM risk factors, potentially paving the way for further investigations and advancements in prenatal care.
  As a Research Assistant, the successful candidate work with the project Principal Investigator (PI) and co-investigators (Co-I’s) to perform a systematic literature review on GDM prevalence in Kenya, create data pipelines to move data from The Aga Khan University HER to the UZIMA-DS cloud platform in addition to performing data analysis tasks and Machine Learning model development. Additionally, the Research Assistant will be responsible for crafting and defending their Master's proposal based on the study's concept, as well as preparing and submitting their Master's thesis in alignment with the study's findings. The Research assistant will be expected to be part of preparation of manuscripts for publication with guidance from the project PI and Co-I’s.
  Responsibilities
  • Contribute to the development of code, computational tools and database for mining and visualizing datasets resulting from the GDM study.
  • Contribute to drafting manuscripts in collaboration with the project PI and co-I’s.
  • Presente compelling, validated progress to all levels of the organization, including peers, senior management, and the UZIMA-DS research hub.
  • Use the project data and resources to develop and address research questions, implement research, and publish scientific papers.
  • Participate in regular, debrief meetings as will be instructed by the project PI.
  • Prepare and defend Masters proposal based on this project.
  • Prepare a master’s thesis based on the output of the GDM study.
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  Requirements
  • Enrolled for a master’s degree in computer science, Statistics/Biostatistics, Mathematics or an equivalent in a recognized university.
  • Have completed a bachelor’s degree in Statistics/Biostatistics, Mathematics, Computer Science, or an equivalent in a recognized university.
  • Proficiency in using one or more programming or scripting languages to work with data such as R, Python, Matlab and stata.
  • Strong knowledge and experience in conducting systematic literature search, data collection, analysis, and attention to details.
  • Experience in data analysis using R, Python, Matlab , stata or other statistical software.
  • Experience in supporting or writing research grants and proposals (track record of winning research grants is desirable)
  • Experience conducting own research studies and managing projects from start to completion.

• About the company

  Aga Khan University is a non-profit institution and an agency of the Aga Khan Development Network.

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