Job Information
Organisation/Company: Trinity College Dublin
Department: Mechanical, Manufacturing & Biomedical Engineering
Research Field: Engineering » Thermal engineering; Engineering » Mechanical engineering; Computer science; Physics » Applied physics
Researcher Profile: First Stage Researcher (R1)
Positions: PhD Positions
Application Deadline: 30 Sep 2026 - 23:59 (Europe/Dublin)
Country: Ireland
Type of Contract: Temporary
Job Status: Full-time
Is the job funded through the EU Research Framework Programme?: Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure?: No
Offer Description
Post Summary: The Gibbons Lab at Trinity College Dublin is seeking a PhD student to join an ongoing Royal Society–Research Ireland University Research Fellowship project focused on Machine Learning for the Design of Additively Manufactured Two-Phase Heat Transfer Surfaces.
From the processor in your phone to the AI compute in a data centre, every high-power electronic device faces the same limit: heat. This has created an urgent demand for next-generation heat transfer surfaces capable of handling ultra-high heat fluxes. One of the most promising approaches is flow boiling, in which a working fluid circulates over a heated surface and undergoes phase change, enabling exceptionally high heat transfer via the latent heat of vaporisation. The question nobody can currently answer is what those heated surfaces should look like.
Additive manufacturing enables the fabrication of spatially complex metal structures with features on the micron-scale. However, our capacity to intelligently design these surfaces is limited, because the models that predict boiling performance only work for the simple shapes that have been empirically tested.
The successful PhD student will conduct research in two-phase flows, with a focus on physics-informed machine learning to model flow boiling surface performance and integrate it with topology optimisation for intelligent design of phase-change surfaces.
The researcher will work closely with other members of a multidisciplinary project team including PIs and postgraduate researchers within this research cluster. This is an innovative cutting-edge project which will yield transformative developments in the field of two-phase flow. The Gibbons research group is known for their excellence in two-phase flow, heat transfer and surface engineering in Ireland and internationally. Funding is offered for 48 months. This post is held in the School of Engineering and Discipline of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin.
Funding: Fully funded for 48 months — €25,000 per annum stipend plus PhD registration fees, covered for both EU and non-EU students.
Candidate profile: Applicants should hold an honours degree in mechanical, chemical or aerospace engineering, applied mathematics, physics, computer science, or a related discipline. Experience across the full project is not expected — training is provided.
How to apply: The application is in two parts. (1) Complete the application form: https://forms.cloud.microsoft/e/uqzauBzwqR. (2) Send one combined PDF containing your CV, academic transcripts, a short cover letter (maximum one page), and the contact details of two referees to gibbonm3@tcd.ie. Full post specification: https://www.gibbonslab.com/s/Gibbons-Lab-PhD-Studentship-Machine-Learning-for-Two-phase-Heat-Transfer.pdf. Closing 30 September 2026; applications are reviewed on a rolling basis.
Where to apply
Requirements
Research Fields / Education Level:
- Engineering — Bachelor Degree or equivalent
- Physics — Bachelor Degree or equivalent
- Computer science — Bachelor Degree or equivalent
- Mathematics — Bachelor Degree or equivalent
Skills/Qualifications
Essential: strong mathematical and analytical ability; programming experience, ideally in Python; demonstrable interest in heat transfer, machine learning, optimisation, or computational modelling; ability to plan, prioritise and meet deadlines.
Desirable: coursework or project experience in machine learning, data science or numerical methods; heat transfer, fluid mechanics or thermodynamics background; experience with CFD (ANSYS Fluent, OpenFOAM, COMSOL) or optimisation methods; laboratory experience in thermal-fluid measurement; familiarity with additive manufacturing; peer-reviewed publications or conference presentations.
Candidates are not expected to possess all of the above. The project sits between thermal science and machine learning, and applications are particularly welcome from candidates strong in one area and eager to train in the other. Training is provided.
Languages: ENGLISH — Level: Excellent
Years of Research Experience: None
Work Location(s)
Number of offers available: 1
Company/Institute: Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin
Country: Ireland
City: Dublin
Contact
State/Province: Dublin
City: Dublin
Website: https://www.tcd.ie/mecheng/staff/academic-staff/gibbonm3/
Street: Parsons Building, Trinity College Dublin,
Postal Code: D02PN40
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