R Allmendinger

This fully-funded PhD studentship at The University of Manchester is part of cohort 3 of the EPSRC Centre for Doctoral Training (CDT) in Developing National Capability for Materials 4.0, in partnership with the Henry Royce Institute. The project focuses on the development of multi-modal AI models for the design and realisation of advanced ferroelectric devices, leveraging cutting-edge computational and machine learning techniques to explore the rich physics of interfacial ferroelectrics. Interfacial ferroelectrics are a novel class of materials formed by stacking two-dimensional van der Waals crystals, creating symmetry-breaking interfaces that enable switchable polarisation without conventional lattice distortions. These heterostructures are highly resilient to charge trapping, mechanical strain, and long-term degradation, and their multi-level, dynamically switchable polarisation states open new possibilities for field-tuneable devices, including reconfigurable optoelectronics, ferroelectric tunnel junctions (fTJs) as analogue synapses, and two-dimensional multiferroics. The project will investigate multilayer functional devices built from stacked monolayer semiconductors and magnetic materials. High-throughput ab-initio Density Functional Theory (DFT) will be used to determine layer-resolved properties such as band edges, dipoles, and magnetic moments. Machine learning interatomic potentials (MLIPs), including Atomic Cluster Expansion (ACE) models, will efficiently scan the chemistry, stacking, and twist space to identify structures with strong ferroelectric responses. The student will develop multi-modal, charge-aware MLIPs trained on the DFT corpus to predict energies, forces, and local charge densities, quantifying the impact of defects and impurities on collective polarisation and switching pathways. These models have broader applications in nano-catalysis and orbital-free DFT. As a PhD researcher, you will join a vibrant community within the Materials 4.0 CDT and the National Graphene Institute, participating in seminars, reading groups, and hands-on training in modern theory of polarisation, DFT/ASE with QE or VASP, workflow automation, and MLIP development with ACE and MACE. You will gain expertise in vdW-inclusive DFT/MD, NEB for switching barriers, LAMMPS/MACE simulations, HPC best practices, and FAIR-data management. The studentship covers full tuition fees (home & international), a tax-free stipend of at least £20,780 plus London allowance if applicable, and a research training support grant. All nationalities are welcome to apply, with up to 30% of studentships available for outstanding international candidates. The CDT is committed to Equality, Diversity, and Inclusion, and encourages applications from underrepresented groups. Applicants should have a strong academic background in Physics, Materials Science, Engineering, Computer Science, or a related field, with experience in computational modeling, DFT, or machine learning highly desirable. English language proficiency is required. The application deadline is March 3, 2026. For general enquiries, contact [email protected]. For technical questions, reach out to Dr. James McHugh ([email protected]). Apply online via the University of Manchester portal, selecting Postgraduate Research, the 2026/27 academic year, and CDT in Materials 4.0.

This fully funded PhD project at The University of Manchester, in collaboration with Honda Research Institute (HRI) Europe, aims to develop an adaptive optimization framework for complex socio-technical systems, with a particular focus on energy distribution networks for electric vehicles (EVs). These systems present large, high-dimensional search spaces and require balancing multiple, often conflicting and expensive-to-evaluate objectives such as fairness, explainability, user satisfaction, and environmental impact. Current industry approaches are typically rule-based, slow, and costly, motivating the need for advanced optimization techniques. Building on prior research in expensive, multi-objective, and high-dimensional optimization, the project will explore guided local and global search strategies, intelligent variable subset selection, and the integration of socially derived criteria into optimization objectives. The research will leverage Bayesian Optimization and Gaussian Processes to efficiently navigate expensive search landscapes, exploiting correlations and problem properties to optimize under limited evaluation budgets. The successful candidate will have opportunities to visit HRI Europe and join their global PhD cohort, gaining exposure to industry-driven research and innovation. Applicants should have a strong background in optimization, ideally with experience in multi-objective and expensive optimization, and familiarity with Gaussian Processes. The program is part of the AI UKRI CDT, offering full funding including home tuition fees and a tax-free stipend at the UKRI rate (£20,780 for 2025/26). The start date is September 2026. The University of Manchester is committed to equality, diversity, and inclusion, encouraging applicants from all backgrounds and offering flexible study options. Application requires submission of transcripts, CV, supporting statement, and referee contact details via the university portal. For more information, visit the project and company webpages.

This fully funded PhD project at The University of Manchester, in collaboration with the Honda Research Institute Europe (HRI-EU), focuses on developing advanced machine learning methods for expensive, high-dimensional multi-objective optimisation. The research addresses complex socio-technical systems, such as energy distribution networks for electric vehicles, where decision spaces are vast and objectives like efficiency, fairness, explainability, user satisfaction, and environmental impact must be balanced. The project aims to overcome the limitations of current rule-based approaches by creating statistics-driven and learning-based optimisation methods. Techniques such as Bayesian optimisation with Gaussian process surrogate models will be explored to efficiently navigate high-dimensional design spaces and make optimal decisions under limited evaluation budgets. The research will build on recent advances in expensive optimisation, multi-objective surrogate modelling, and adaptive variable subset optimisation, leveraging both local and global search strategies. Key research objectives include: exploring high-dimensional search spaces using intelligent variable subset selection, integrating socially derived criteria (trust, explainability, fairness), and coping with expensive evaluations by exploiting correlations in the search landscape. The successful candidate will have opportunities for regular visits to HRI-EU and to join Honda’s international PhD research community. Applicants should have a strong quantitative background in machine learning, statistics, optimisation, or applied mathematics. Experience with Bayesian optimisation or Gaussian processes is highly desirable. The project is part of the UKRI AI CDT in Decision Making for Complex Systems and offers a stipend of £31,000 for students eligible for home fee status. The start date is September 2026. To apply, candidates must submit a complete application through the University of Manchester Application Portal, specifying the project title and supervisor names, and uploading all required documents (transcripts, CV, supporting statement, and referee contact details). The university values equality, diversity, and inclusion, and encourages applicants from all backgrounds. Flexible and part-time study options are available.