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Artificial Intelligence Enabled IVF

   School of Medical Sciences

   Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

Background of Scholarship:

Applications are invited for a PhD scholarship opportunity on exploring ways to improve in-vitro fertilization (IVF) through a combination of computer vision-based and machine learning techniques. This scholarship is part of a research project ̶ Improving IVF success rates through machine learning ̶ supported by the MBIE smart ideas grant. The successful applicant must be eligible to enroll at the University of Auckland and will be jointly supervised by Drs. Nicholas Knowlton and Lynsey Cree.

Primary Purpose of the Position

Undertake research activities as required to advance understanding of what factors influence successful IVF procedures. The project will leverage millions of embryo images along with a deep clinical information plus next-gen sequencing data to arrive at potential solutions. This inter-disciplinary project has scope to incorporate bioinformatics, computer vision, machine learning and clinical diagnostic development. Successful applicants will have a strong programming base and be motivated to help improve fertility rates.

We propose that the successful applicant will:

1. Undertake a comprehensive review on approaches in AI enabled IVF including the role of clinical factors on implantation and live birth rates.

2. Extend our understanding of the role of mosaicism on embryo development including developmental timings.

3. Develop and validate several prognostic algorithms to predict embryo implantation.

Knowledge & Skills


• Expertise in computer science, ideally CV models based on single shot detectors or Feature Pyramid

Networks (FPN)

• Expertise in quantitative and qualitative data analysis

• Excellent written and verbal communication skills across a range of technical and non-technical personnel.

Education & Experience


• A qualification in computer science or related computational discipline such as information systems, statistics,

or computer engineering to meet the eligibility criteria for entry into the PhD programme for the University of


• Strong interest in health and using computer science to enhance health outcomes, in particular sub-fertile


Personal Attributes

• Independent and flexible work practices

• Attention to quality and accuracy of work

Application documents required:

• CV

• Transcripts of previous degrees

• Two references of character

Funding Notes

PhD is fully funded and includes a stipend for 3.5 years.

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