All new chemicals are tested for their potential to cause DNA damage (genotoxicity), as this is linked to cancer. A tiered approach is used, with the chemicals first being tested in vitro. Those that induce DNA damage (test positives) are then usually tested again using animals. Some genotoxic agents give positive results in vitro at relatively high doses, but do not cause DNA damage at low doses more typical of human exposure levels. Identifying the dose below which DNA damage does not occur is important because understanding how this relates to human exposure levels can provide an opportunity to avoid using animals. This project will use an in vitro system to assess the genotoxic potential for a wide range of chemicals, helping to establish optimum testing strategies for evaluating the effects of dose and potentially reducing animal use.

The most effective means of understanding strongly-interacting fundamental particles, such as quarks and gluons, is via numerical simulation of lattice QCD, a discrete formulation of quantum field theory, using state-of-the-art high performance computing. Working within the Swansea Lattice Field Theory group, and as part of the international FASTSUM collaboration, the PhD student will apply these techniques to one of a range of problems: our interests include excitations in the hot medium present in the first moments of the Universe and now briefly recreated in collisions between highly relatiyistic nuclei; theories of symmetry breaking in which the Higgs is not fundamental but rather a composite strongly-bound state; theories with a non-zero particle density applicable to the dense material found within nuclei and in neutron star cores; and theories of relativistic fermions moving in a two-dimensional plane, with applications to condensed matter /materials physics to systems such as topological insulators and graphene.

This project aims to evaluate the feasibility of establishing local manufacturing capabilities for perovskite solar cells (PSCs) by investigating supply chains, indigenous materials, and sustainable development pathways. In addition to exploring locally sourced, sustainable materials for PSC production, the research will quantify waste generation and assess the potential for using secondary materials within the manufacturing process. This dual focus on waste reduction and material reuse seeks to address environmental impacts associated with PSC production, reduce reliance on imported, high-impact materials, and strengthen regional supply chains. By examining the availability, compatibility, and performance of alternative, locally derived materials, this work aligns with goals for a sustainable energy transition and energy access in developing regions, advancing scalable manufacturing solutions that support economic resilience, lower production costs, and promote environmentally responsible technology deployment

The key competitive driver for the PhD is to study Industry 4.0 total connectivity concepts within a specific, knowledge intense industrial background. Industry 4.0 is the fourth industrial revolution that strives for smart manufacturing and autonomy. As with all new innovations, there are a lot of underlying unanswered questions to this shift in paradigm which this research aims to examine and address. On the technological side this will include Cyber Physical Systems, Industrial Internet of Things, Big Data & Monitoring, Machine Learning and others. This research will provide scientific underpinnings for Industry 4.0 approaches to knowledge intense industrial applications. This PhD is in partnership with Tata Steel. The underlying research will focus on developing a system model for the Cold Rolling Mills in their Port Talbot plant to support the Industry 4.0 standard. The Cold Rolling Mills is the process of condensing steel into thin, ductile coils. Currently, there is heavy automation, but decisions are made from human interaction instead of any form of autonomy. The key objective will be researching into the creation of a digital framework to model the total connectivity concepts that Industry 4.0 requires to enable such autonomy. To achieve our goals, we will use a model-driven approach to create such a digital framework. Model-driven approaches are known to be essential in achieving a level of abstraction that allows to deal with the complexity of the tasks ahead in an efficient way. Our work will include exploring the use of digital twins and digital threads to create realistic simulations that will use real time data to accurately replicate the Cold Rolling Mill system digitally. This will allow us to commit changes to the digital form without any impact on the physical form. For example, we may simulate the increase pressure levels on the rolls to see the impact it has without any risks of the actual physical assets being damaged. Additionally, the rolls in the Cold Rolling Mills suffer massively from wear and must be refurbished often. Using the data collected from Tata Steel and our modelling techniques, this research also aims to optimize and increase efficiency of Tata's roll stock.

Recent changes in the earth's climate have been associated with numerous changes in animal and plant populations: for example, as temperatures have risen, the average timing of key events such as breeding has shifted substantially earlier in many species. However, we still have only limited knowledge of the actual mechanisms driving such responses to climate change. Importantly, we need to understand reasons for the changes that are occurring in the specific characteristics or traits (such as timing, growth or fecundity) that determine individuals' fitness and hence that shape a population's overall rate of growth or decline. Changes in a population's characteristics over time could be driven by one, or more, of three different processes: (i) population-level demographic changes in the representation of different age groups, each of which may display different trait values; (ii) individual-level phenotypic plasticity, whereby an individual expresses a different value of a trait dependent on the environmental conditions it experiences; (iii) genetic-level evolutionary change, whereby the genetic composition of the population changes in response to climate change favouring different genes under novel environmental conditions; These contrasting mechanisms can all generate a trend in the average value of a trait over time, but they have different implications for continued responses to a changing environment, and for whether or not a population can 'keep up' with climate change. Dissecting their relative contributions requires detailed, long-term data-sets. Partly because of this, this analysis of the relative contributions from each mechanism to observed responses to climate change have not yet, to our knowledge, been quantified for any wild animal population. We propose to address this gap using a study of a wild population of red deer (Cervus elaphus) on the Isle of Rum, NW Scotland. This project will enable an analysis of nearly four decades of data on more than 4000 individually-monitored deer. We will first explore the direct and indirect effects of climatic and/or vegetation variation on deer traits; this will require extensive assessments of changes in vegetation properties, for which we will use long-term ground-level and remote sensing data. Our primary aim is then to quantify the relative contributions of the three processes listed above to these trends. Finally, we will use insights from these analyses to create models of the dynamics of the population and of individual traits, and hence to generate predictions for future rates of population growth or decline. The Rum red deer project is arguably the best data resource anywhere for this analysis. The new data collection we propose will provide detailed indications of changes in vegetation properties over the study period, as well as sufficient numbers of records of the morphology, reproductive success, survival, timing characteristics and behaviour of individuals across different decades to test the mechanisms outlined above. Our multi-generation, genetically-validated pedigree will enable us to estimate the extent to which individual characteristics are under genetic control, whether climate change has altered patterns of natural selection on these characteristics, and whether the population has shown an evolutionary response to this selection. We also have extensive experience of the complex statistical and modelling techniques required for the analysis. Our ambition is to provide the most comprehensive analysis to date of how a wild animal population is responding to climate change and whether there are limits to its natural capacity to change. Our study will be the first of its kind. Given the importance of large herbivores to the way ecosystems function, our results will have implications for future management policy as well as offering fundamental insights into the mechanisms by which climate change affects wild populations.