Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

DynOMIS aims to elucidate the antigen selection mechanisms of the adaptive immune system at the molecular level in the highly complex cellular environment. Major histocompatibility complex class I molecules (MHC-I) is a key mediator of adaptive immunity, the cell’s arsenal against infectious pathogens and malignant transformations. MHC-I present antigenic peptides to cytotoxic T lymphocytes at the cell surface, which in turn unleash their cytotoxic apparatus only when peptides from non-healthy proteins are recognized. This process is the result of an equally important peptide selecting function in the early secretory pathway, a mechanism that has not been clearly understood in spite of its fundamental role in vaccination. Deep understanding of the exact mechanisms that drive peptide selection by MHC-I will help to predict immunoprotective epitopes in infections and cancer, which will in turn pave the way for the development of more effective T cell-targeting vaccines and biomarkers to stratify patients’ suitability for immunotherapy. DynOMIS will employ a sophisticated, interdisciplinary approach that integrates quantitative computational systems modelling to identify molecular mechanism from cellular biochemical information, experimental investigation of the structure and dynamics of peptide-bound MHC-I over a large range of timescales, and state-of-the-art molecular dynamics simulations and free energy calculations to elucidate the thermodynamic basis of the peptide selection mechanism in the context of their interactions with cellular cofactors. To this end, DynOMIS will be carried out by an experienced researcher at a world-leading interdisciplinary group comprising molecular immunologists, structural biologists, computational chemists, and industrial partners with a strong focus on clinically relevant immunological research.

The student will conduct research training through the UKRI MINDS Centre for Doctoral Training, a four-year integrated PhD programme, with a focus on the Embedded Artificial Intelligence theme. The research conducted in this theme will investigate how to efficiently and reliably embed algorithmic techniques such as deep learning, Bayesian inference and optimisation in low-power devices. This may address challenges around low-shot and transfer learning, and managing performance/efficiency trade-offs.

Architecturally heterogeneous multi-material multi-principal element alloy (MPEA) composites represent a unique class of heterostructured materials with great potential to evade the strength–ductility trade-off dilemma. However, most conventional processing routes involving high temperatures (e.g., casting, additive manufacturing, and powder metallurgy) suffer from various metallurgical issues such as excessive elemental diffusion, segregation, hard intermetallic phase formation, cracking, and poor densification. To mitigate these challenges, the project suggests prefabricating various MPEA powders into billets using cold spraying (CS) and then employing solid-state friction stir processing (FSP) to achieve microstructural densification. This approach will initiate various dynamic recrystallizations in different compositional domains, allowing us to engineer architecturally heterogeneous multi-material MPEA composites. In general, this project will be implemented in phases, including optimizing process, controlling the heterostructure, evaluating mechanical performances from ambient to cryogenic temperatures, and elucidating deformation mechanisms. The results can be expected to build up the “processing–microstructure–mechanical behavior” correlations of the newly developed multi-material MPEA composites in a quantitative manner. This will facilitate the design and fabrication of similar heterostructured composites, thereby impacting a wide range of industrial sectors, e.g., transport, defense, nuclear, and manufacturing.

The technical objectives of the project are: 1) enhance and extend an integrated aircraft design process building on an existing framework; introduce into the design process a range of disciplinary models of varying fidelity that will allow for an accurate yet rapid engineering design; 2) develop and implement passive and active control technologies that take advantage of wing elasticity in order to reshape the wing for optimal aerodynamic performance and minimal structural loads per flight condition; and limit the peak loads to improve passenger comfort/reduce the load factor, reduce the structural weight and to improve the fatigue resistance.