Postdoc: AI-Driven Atomic-Scale Materials Characterisation

The University of Sydney; Sydney; On-site; AUD 118,000 - 126,000; Full time

Job summary

The University of Sydney invites applications for a Postdoctoral Research Associate in AI Enabled Atomic-Scale Materials Characterisation. The role focuses on AI-driven analysis of atomic-scale structure using advanced microscopy and APT within a collaborative, international team.

You will develop quantitative, AI-powered methods to reveal structure–property relationships in advanced materials, supervise HDR students, and contribute to high-impact publications and conference presentations.

Qualifications

• PhD in a relevant discipline.
• Expertise in advanced microscopy and microanalysis, especially APT.
• Experience with TEM/SEM/EBSD/XRD and complex materials datasets.
• Proficiency in quantitative analysis, computational methods and programming with AI/ML exposure.
• Strong publication record and ability to work in multidisciplinary teams.

Responsibilities

• Conduct research using advanced microscopy and characterisation to study atomic-scale structure and chemistry.
• Develop quantitative, AI-enabled approaches for materials characterisation and data analysis.
• Collaborate with multidisciplinary teams, industry partners and international collaborators.
• Contribute to supervision, training, grant development and dissemination through journals and conferences.

Skills

AI

Machine Learning

Programming

Quantitative analysis

Data-driven materials research

Education

PhD in Materials Science and Engineering / Physics / Materials Characterisation / Computational Materials Science

Tools

Atom Probe Tomography (APT)

TEM

SEM

EBSD

XRD

Job description

The University of Sydney invites applications for a Postdoctoral Research Associate in AI Enabled Atomic-Scale Materials Characterisation. The role focuses on AI-driven analysis of atomic-scale structure using advanced microscopy and APT within a collaborative, international team.

You will develop quantitative, AI-powered methods to reveal structure–property relationships in advanced materials, supervise HDR students, and contribute to high-impact publications and conference presentations.