[School of Engineering PhD Scholarships] Physics-Informed Machine Learning for Image-Based Property Prediction in Aerogels and Aerogel Composites

About the project

About the Project Physics-Informed Machine Learning for Image-Based Property Prediction in Aerogels and Aerogel Composites Thermal insulation is important in applications ranging from buildings and industrial equipment to cryogenic systems, high-temperature processes and oil and gas pipelines. Improving insulation can reduce energy demand and support the transition towards net zero. Silica aerogels and aerogel composites offer strong insulation performance at reduced thickness, but designing these materials requires a better understanding of how their internal structures control heat transfer. This PhD project will combine advanced imaging, physics-based simulation and machine learning to predict the thermal conductivity of aerogels and aerogel composites. You will investigate how pore structure, phase connectivity, cracks and reinforcement architecture influence thermal performance, linking microscopic features to macroscopic material properties. The project is primarily computational, complemented by targeted imaging and commercially sourced samples. You will analyse scanning electron microscopy (SEM) images to characterise silica aerogel microstructures, considering focused ion beam SEM (FIB-SEM) where additional three-dimensional information is needed. Image analysis and digital microstructure generation will support voxelised representations for heat-transfer modelling. These models will estimate effective aerogel properties and be benchmarked against available experimental measurements and literature data. At larger scales, you will use X-ray microCT images to investigate aerogel composite structures. Since these images generally cannot resolve aerogel nanopores, properties obtained from smaller-scale models will inform composite-scale simulations. You will then develop machine-learning or surrogate models, informed by physical knowledge and simulation data, to accelerate property prediction and identify the structural features most important for insulation performance. You will be supervised by Dr Mehrdad Vasheghani Farahani in Department of Chemical Engineering and Prof. Philip Withers in Department of Materials, combining expertise in porous media transport modelling and advanced imaging. Training will cover image processing, microscopy and microCT data interpretation, digital microstructure analysis, numerical heat transfer simulation and machine learning. The project will draw on Manchester’s imaging facilities, local workstations and University computing resources. Expected outcomes include computational tools for thermal-property prediction, improved understanding of structure-property relationships, and guidelines for designing aerogel composites for superinsulation. You will have opportunities to develop publications and present findings at relevant conferences, including InterPore and the International Seminar on Aerogels. The project would suit applicants from Chemical Engineering, Materials Science, Physics, Chemistry, Mechanical Engineering or related disciplines. An interest in scientific programming, numerical modelling, heat transfer, porous materials, image analysis or machine learning would be particularly relevant. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Chemical Engineering, Materials Science, Physics, Chemistry, Mechanical Engineering (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Chemical Engineering, Materials Science, Physics, Chemistry, Mechanical Engineering(or international equivalent). Previous research experience or a strong interest in scientific programming, numerical modelling, heat transfer, image analysis or machine learning would be advantageous. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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