University of Sydney
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Fully Funded PhD Positions in AI for Engineering and Sustainable Construction Materials University of Sydney in Australia
Degree Level
PhD
Field of study
Computer Science
Funding
Multiple fully funded PhD positions are advertised.
Country
Australia
University
University of Sydney

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About this position
Multiple fully funded PhD positions are available at the University of Sydney, School of Civil Engineering, with Dr Xingquan (Jasmine) Wang. The project theme is AI for Engineering and Sustainable Construction Materials, with a start date in 2027.
Research directions include AI-enabled sustainable construction materials and smart construction/additive manufacturing. Topics mentioned include AI-guided design and optimisation of low-carbon and carbon-negative materials, computational modelling and data-driven prediction of material properties and performance, AI-assisted design and experimental testing of advanced composites and cementitious materials, additive manufacturing and 3D printing of cementitious and polymeric materials, process monitoring and optimisation, quality control for digital construction, and integration of experiments, sensors, and machine learning for intelligent manufacturing.
The positions are fully funded. The post highlights applicants from civil engineering, mechanical engineering, materials science, computer science, or related engineering backgrounds. Preferred skills include machine learning, Python programming, computational mechanics, molecular dynamics, additive manufacturing, and materials characterization, along with strong problem-solving ability, curiosity, and initiative.
To apply, email [email protected] with a CV, publication list, contact details for 2-3 referees, transcripts, and supporting materials. Use the subject line: [PhD Application] Your Name - Area(s) of Interest.
Funding details
Multiple fully funded PhD positions are advertised.
What's required
Background in civil engineering, mechanical engineering, materials science, computer science, or a related engineering field. Desired experience includes machine learning and programming in Python, computational mechanics, molecular dynamics, additive manufacturing, and materials characterization. Strong problem-solving ability, curiosity, and initiative are emphasized.
How to apply
Email Xingquan Wang with your CV, publication list, contact details for 2-3 referees, transcripts, and supporting materials. Use the subject line: [PhD Application] Your Name - Area(s) of Interest.
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