Kozeta Miliku, Queen's University

Research

Birth Cohorts Child Multimorbidity Developmental And Social Factors Exposome And Environmental Determinants Human Development, Child And Youth Health Longitudinal Development Population And Health Statistical And Machine Learning

Latest funding

Leveraging Interpretable Machine Learning Approaches to Improve the Health of Children with Multimorbidity.
CIHR · 2024 · Principal investigator
$142,000

All 1 grants and awards

Leveraging Interpretable Machine Learning Approaches to Improve the Health of Children with Multimorbidity.

Principal investigatorCIHR2024Operating Grant: ECRs in Human Development, Child and Youth Health

Principal investigators: Lu, Zihang; Miliku, Kozeta

Keywords: Birth Cohorts; Child Multimorbidity; Developmental And Social Factors; Exposome And Environmental Determinants; Human Development, Child And Youth Health; Longitudinal Development; Population And Health; Statistical And Machine Learning

$142,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

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Applying for

Mention their CIHR grant from 2024

Subject: Prospective PhD student: Birth Cohorts Dear Professor Miliku, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. Your research on birth cohorts connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your CIHR-funded project "Leveraging Interpretable Machine Learning Approaches to Improve the Health of Children with Multimorbidity." (2024), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]