Andrew Kao

This interdisciplinary PhD project at the University of Greenwich offers an exciting opportunity to advance computational methods for evolving-domain problems, with a particular focus on phase transitions in materials. The research unites applied mathematics, computational mathematics, and engineering, targeting the development and improvement of numerical approaches for partial differential equations (PDEs) defined on domains with time-dependent interfaces, known as free-boundary problems. Physical phenomena such as the formation of oxides or solid-electrolyte interphases in battery electrodes involve propagating interfaces between distinct material phases. These interfaces are often described by highly non-linear PDEs, and their computational handling remains a significant challenge. The project aims to further develop the cut-finite-element method, which treats phase boundaries as sharp interfaces moving across a fixed finite-element mesh. The focus will be on enhancing the accuracy and efficiency of this computational approach for a range of material systems exhibiting multi-physics behaviour, including chemo-mechanical and magneto-mechanical systems. The successful candidate will join the Computational Science and Engineering Group (CSEG) within the Faculty of Engineering and Science, becoming part of a dynamic and growing research team with extensive expertise in computational materials modelling and phase transformation processes. The project is supervised by Dr Mikhail Poluektov, Prof Andrew Kao, and Dr Ivars Krastins, ensuring strong academic guidance and support. This studentship is fully funded by the M34Impact programme, a £9 million Expanding Excellence in England (E3) grant. The funding package includes a generous stipend, London weighting, enhanced bursary, and a contribution to tuition fees at the University Home Rate. International applicants may need to cover the remainder of tuition fees unless exceptionally supported by the programme. The bursary is for three years, with the possibility of a 12-month extension if progress is satisfactory. Applicants should have a strong academic background in applied mathematics, computational mathematics, or engineering, with experience in numerical methods for PDEs and materials modelling. A first or upper second class degree (or equivalent) in a relevant subject is required. International candidates must meet English language requirements and may be responsible for additional tuition fees. The application deadline is March 31, 2026. Interested candidates should apply online via the University of Greenwich portal, submitting a CV, academic transcripts, and a cover letter. For further details, refer to the FindAPhD project link provided.

[£22,780 to £24,780 per annum bursary for 3 years (potential extension up to 12 months); contribution to tuition fees at University Home Rate (£5,006/year); international applicants may need to pay remainder tuition fee unless covered by M 3 4Impact.] This interdisciplinary PhD studentship at the University of Greenwich offers an exciting opportunity to advance computational methods for evolving-domain problems, with a particular focus on phase transitions in materials. The project bridges applied mathematics, computational mathematics, and engineering, targeting the development and improvement of numerical approaches for partial differential equations (PDEs) defined on domains with time-dependent interfaces, known as free-boundary problems. Physical phenomena such as the formation of oxides and solid-electrolyte interphases in battery electrodes involve propagating interfaces between distinct material phases. These interfaces are modeled as sharp boundaries moving across fixed finite-element meshes, presenting significant computational challenges, especially in highly non-linear cases. The project aims to further develop the cut-finite-element method to enhance accuracy and efficiency in simulating phase transitions in materials, including multi-physics systems like chemo-mechanical and magneto-mechanical processes. The successful candidate will join the Computational Science and Engineering Group (CSEG), a dynamic research team with expertise in computational materials modelling and phase transformation processes. The studentship is fully funded by the M 3 4Impact programme, part of a £9 million Expanding Excellence in England (E3) grant, providing access to training and research initiatives. Supervision will be provided by Dr Mikhail Poluektov, Prof Andrew Kao, and Dr Ivars Krastins, ensuring strong academic guidance and support. Funding includes a bursary of £22,780 to £24,780 per annum for three years, with a possible extension of up to 12 months. Tuition fees are covered at the University Home Rate (£5,006/year), and exceptional international applicants may have their full tuition fees covered. International applicants should note that they may need to pay the remainder tuition fee unless covered by the funding programme. Applicants should possess a strong academic background in applied mathematics, computational mathematics, or engineering, with experience in numerical methods for PDEs and mechanics of materials. A first-class or upper second-class undergraduate degree (or equivalent) in mathematics, physics, engineering, or a related discipline is preferred. Experience with finite element methods, computational modelling, or phase transitions in materials is advantageous. English language requirements may apply for international candidates. To apply, submit your application online via the University of Greenwich portal, including your CV, academic transcripts, and a cover letter outlining your suitability for the project. The application deadline is 17 April 2026. For further information, contact the supervisors or visit the project link provided.

The University of Greenwich invites applications for a fully funded PhD studentship as part of the pioneering 'Self-Driving Microscope' project, a major 8-year initiative aimed at revolutionising the detection and prediction of stochastic failure in solid-state batteries. This project addresses a critical national challenge: the unpredictable microscopic flaws that cause catastrophic battery failure, such as dendrite penetration and structural cracking, which are invisible to standard tests. As the founding PhD student in the new BASE (Beamlines for Autonomous Science and Engineering) Laboratory, you will play a central role in designing and building the core predictive engine for an autonomous, AI-driven X-ray imaging platform. Your research will span the intersection of physics-informed artificial intelligence, materials science, and computational simulation. Key objectives include constructing a multi-scale training dataset using 3D X-ray tomograms of advanced solid-state cells, developing advanced AI models (such as Graph Neural Networks) to distinguish benign aging from true failure precursors, and integrating physics-based signatures using high-performance computing solvers like OpenImpala. You will also augment experimental data with generative AI models to improve robustness and generalisation. The BASE Laboratory is structured with a computational core at the University of Greenwich and an experimental hub at the Rutherford Appleton Laboratory, offering direct access to national facilities and collaborative opportunities. The project is a cornerstone of the Computational Science and Engineering Group’s goals and is embedded within the M34Impact doctoral cohort. Supervision is provided by Dr James Le Houx (Faraday Institution Emerging Leader Fellow), Prof. Andrew Kao, and Dr. Mikhail Poluektov, ensuring expert guidance in simulation, AI, and battery imaging. Funding is fully secured through the £9M Research England-funded M34Impact expansion programme. The studentship covers a generous stipend (Year 1: £24,780; subsequent years in line with UKRI rates plus London weighting and enhanced bursary) and a tuition fee contribution equivalent to the University Home Rate (£5,006/year). International applicants may need to pay the remainder tuition fee unless covered by M34Impact. The bursary is for 3 years with a possible extension of up to 12 months, subject to satisfactory progress. Applicants should possess a strong academic background in physics, materials science, engineering, computer science, or mathematics, with experience in computational modelling, machine learning, or AI highly desirable. Programming skills and familiarity with high-performance computing are advantageous. International applicants must meet English language requirements and may be required to pay additional tuition fees. The application deadline is April 17, 2026. To apply, submit your CV, academic transcripts, and a cover letter via the University of Greenwich portal, referencing M34Impact-MSE2. Informal enquiries to the supervisors are encouraged. This is a unique opportunity to join a dynamic research group and contribute to the next generation of battery technology and autonomous scientific platforms.

[Fully funded studentship with annual stipend of £22,780 to £24,780, supported by the University's £9M Research England-funded M 3 4Impact expansion programme.] The University of Greenwich invites applications for a fully funded PhD studentship focused on the electromagnetic control of metal solidification, with an emphasis on multiscale multiphysics modelling from dendritic to bulk scales. This research addresses a critical step in the manufacture of advanced components for aerospace and automotive industries, where the solidification of liquid alloys determines the microstructure and properties of the final product. Recent advances have shown that electromagnetic fields (EMF) can induce interface-driven forced convection, refining grains, mitigating solute segregation, and modifying intermetallic compound morphology. However, the mechanisms governing these effects are complex and not fully understood, presenting a bottleneck for precise microstructure control. This project aims to develop comprehensive multiscale models to characterize magnetohydrodynamic behaviour under time-varying EMF, leveraging both advanced modelling and experimental insights. Building on collaborations with the UK National Synchrotron Radiation Centre (DIAMOND Light Source project) and the German DAAD project, the research will span from dendritic to bulk scales. Key objectives include: (1) Development of multiphysics coupling models to capture interactions among electromagnetic fields, thermal fields, fluid flow, and solute transport; (2) Integration of dendritic-scale X-ray experiments and ingot-scale casting experiments to establish multiscale numerical models and elucidate dynamic relationships; (3) Controlled fabrication of target crystal structures under EMF using advanced digital tools such as AI, COMSOL Multiphysics, and Python. The project is part of the Computational Science and Engineering Group (CSEG) and the M 3 4Impact doctoral cohort, offering access to expertise in advanced modelling, synchrotron data processing, and AI. The group maintains an extensive international collaboration network, including partners in Germany, France, Latvia, China, and industry, providing opportunities for collaborative research abroad. Funding is provided by the University's £9M Research England-funded M 3 4Impact expansion programme, with an annual stipend of £22,780 to £24,780. The studentship covers tuition and living expenses, enabling you to focus on research and professional development in a supportive environment. Applicants should have a strong academic background in materials science, mechanical engineering, physics, chemical engineering, or related fields. Experience with computational modelling, numerical methods, or solidification science is desirable, and proficiency in digital tools such as AI, COMSOL Multiphysics, or Python is advantageous. English language proficiency must meet University of Greenwich standards. To apply, submit your application via the University of Greenwich portal, including your CV, academic transcripts, and a cover letter outlining your motivation and relevant experience. The deadline for applications is 1 June 2026. For further information, contact the research group or visit the application link provided.

[Fully funded studentship with annual stipend of £22,780 to £24,780, supported by the University's £9M Research England-funded M 3 4Impact expansion programme.] The University of Greenwich invites applications for a fully funded PhD studentship as part of the pioneering 'Self-Driving Microscope' project, aimed at predicting stochastic failure in solid-state batteries using physics-informed AI. This project addresses a critical challenge in battery technology: the unpredictable microscopic flaws that lead to catastrophic failure, such as dendrite penetration and structural cracking, which are often hidden from standard diagnostic tests. As the first PhD student and a founding member of the new BASE (Beamlines for Autonomous Science and Engineering) Laboratory, you will play a central role in designing and building the core predictive engine for an autonomous, AI-piloted X-ray imaging platform. This platform will intelligently hunt for hidden failure points in real-time, advancing the next generation of beamlines and battery diagnostics. The research is at the intersection of AI, physics-based simulation, and materials science. Key objectives include building a multi-scale training dataset using 3D X-ray tomograms of next-generation solid-state cells (such as Li-metal/Li₆PS₅Cl) acquired at the I13-2 beamline (Diamond Light Source). The dataset will capture cells at various stages: pristine, aged, and post-failure. You will develop advanced AI models, including Graph Neural Networks (GNNs), to distinguish benign aging from true failure precursors, retrospectively confirmed as the origin of cracks. The project goes beyond image recognition by integrating physics-based signatures using high-performance computing solvers (like OpenImpala) to transform static 3D porosity maps into dynamic maps of local tortuosity and ionic flux, providing physically-grounded features for AI learning. Additionally, you will augment experimental data with generative AI models (Diffusion models/GANs) to create a diverse library of synthetic microstructures, enhancing model generalisation against noisy, live data. This studentship is fully funded, offering an annual stipend of £22,780 to £24,780, and is supported by the University's £9M Research England-funded M 3 4Impact expansion programme. The project is a cornerstone of the Computational Science and Engineering Group’s (CSEG) goals and will serve as a foundational project for the new BASE Laboratory. You will be embedded within the M 3 4Impact doctoral cohort and co-supervised by Prof. Andrew Kao, whose group provides validated simulation models, and Dr. Mikhail Poluektov. Dr. James Le Houx, Faraday Institution Emerging Leader Fellow at Rutherford Appleton Laboratory (RAL) and co-leader of the UK's Battery Imaging BAG at Diamond Light Source, will also supervise. The BASE Laboratory is structured with a computational core at the University of Greenwich and an experimental hub at RAL, offering direct links to national facilities and a dynamic, collaborative research environment. Applicants should hold a first-class or upper second-class undergraduate degree in a relevant field such as materials science, physics, computer science, or chemistry. Experience with computational modelling, machine learning, or AI is highly desirable. Strong analytical skills and familiarity with scientific programming are preferred. English language proficiency is required; specific test requirements are not mentioned. The application deadline is 17 April 2026. To apply, submit your application via the University of Greenwich portal, including your CV, academic transcripts, and a cover letter outlining your suitability for the project. For further information, contact the supervisors listed in the project description.