Trinity College Dublin
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PhD Studentship in Machine Learning for Two-Phase Heat Transfer Trinity College Dublin in Ireland
Degree Level
PhD
Field of study
Computer Science
Funding
Funding is offered for 48 months for the PhD studentship.
Deadline
Sep 30, 2026
Country
Ireland
University
Trinity College Dublin

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About this position
PhD Studentship in Machine Learning for Two-Phase Heat Transfer at Trinity College Dublin, Ireland.
The Gibbons Lab in the School of Engineering and Discipline of Mechanical, Manufacturing and Biomedical Engineering is recruiting a PhD student for an ongoing Royal Society–Research Ireland University Research Fellowship project on machine learning for the design of additively manufactured two-phase heat transfer surfaces.
The project focuses on two-phase flows, flow boiling, physics-informed machine learning, topology optimisation, and the intelligent design of phase-change surfaces for high-power electronics and data-centre cooling. The research aims to improve predictive models for boiling performance and support the fabrication of spatially complex metal structures using additive manufacturing.
Funding is offered for 48 months. The post is based at Trinity College Dublin in Ireland.
Applicants should submit a CV, academic transcripts, a one-page cover letter explaining their interest in the project, and the names and email addresses of two referees. Applications are reviewed on a rolling basis, and early application is strongly encouraged.
Apply via the Microsoft form and send the combined PDF to [email protected]. Shortlisted candidates will be interviewed by Microsoft Teams or Zoom.
Funding details
Funding is offered for 48 months for the PhD studentship.
What's required
Applicants should be interested in a PhD in two-phase flows, physics-informed machine learning, flow boiling surface performance modelling, and topology optimisation for intelligent design of phase-change surfaces. The post asks for a CV, academic transcripts, a short cover letter, and contact details for two referees. Early application is strongly encouraged; shortlisted candidates will be interviewed by Microsoft Teams or Zoom.
How to apply
Complete the Microsoft application form and then email a single combined PDF containing your CV, academic transcripts, cover letter, and referee details to [email protected]. Name the file Surname_Firstname.pdf.
More information can be found here
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