The project is about using galaxy formation simulations to study the effects of energy injection by Active Galactic Nuclei (AGN) on the properties and evolution of galaxies. We will develop models for AGN feedback that are better physically motivated compared to models generally used currently, and investigate their effects. In particular, we plan to model feedback due to winds from AGN accretion disks, and compare to feedback by AGN jets. The AGN feedback models will be implemented in cosmological simulations of galaxy formation using the SWIFT code.

Optical turbulence in the Earth's atmosphere limits the bandwidth of proposed optical feeder links to orbit, limiting the ability to exploit the full advantages of optical links over radio. By measuring and modelling this dynamic turbulence we are able to explore potential innovative mitigation solutions.

The PhD student will develop and design an eco-friendly rechargeable battery. Artificial Intelligence will be used to design the system based on existing scientific and industrial data. It is highly desirable to use rechargeable batteries to store energy from renewable sources and in consumer electronics and automobiles. This market is dominated by lithium-ion batteries (LIB) which have high efficiency and long life cycles. LIBs are expensive, scarce, and recycling them has a large environmental impact. Using inverse engineering and deep learning, we aim to design next-generation batteries with minimal environmental impact. The successful candidate will combine data analyses with Deep Learning methods and experiment with the state-of-the-art fabrication facilities available at Durham. The candidate will have the opportunity to develop a novel model that correlates the properties of batteries to the composition of materials. This model will also consider the environmental impact of all processes in detail. Because the project has industrial support, the candidate will work closely with the company (WeLoop) to assess the life cycle assessment of existing batteries as a tool to design the next generation batteries. To be successful, a broad skill set is essential, as well as an enthusiasm for experimentation, coding/simulation, and practical engineering.

Jack Marchant's project details are as follows: Title : Arctic forest change in a warming climate Outline: Biomes and the transition zones between them are expected to shift under the influence of a changing climate because of species' range shifts. One such very important transition zone is that of the boreal forest to arctic tundra and the potential for forest advance into tundra with climatic warming. Shifts in dominant vegetation type have profound potential impacts upon both energy balance, as well as sign and magnitude of exchanges of greenhouse gases between land and atmosphere, along what is Earth's most extensive (by area) ecological boundary. These shifts, in turn, may feedback to promote further enhanced warming across Arctic regions and beyond. Since such shifts may be more varied than a simple, uniform, poleward displacement (Rees et al. 2020) key questions requiring urgent attention are: whether or not forest advance is underway around the circumpolar Arctic and, if so, at what rates, where and why? This project will build upon previous, complementary, work of the supervisors in determining forest structure and potential transitions as a result of change, combining both remote sensing and plant ecology approaches, as well as drawing upon recent studies by others at localities around the circumpolar Arctic (Rees et al. 2020, and references therein) as a solid starting point. A remote-sensing-led approach will allow us to expand significantly beyond the currently rather patchy distribution of reported sites and associated studies at localities around the Arctic. This is particularly relevant at the current time when addressing the dearth of relevant information for much of the Russian Arctic at a time of many geopolitical challenges for physical study.

This project aims to elucidate why humans engage in successive reward behaviours, specifically why they often seek and consume rewards (alcohol, food) after having experienced positive outcomes in other areas (winning, achievement). The studies will build on my previous Master's by Research (MRes) project which demonstrated across two experiments that a rewarding outcome in a quiz (positive feedback eliciting positive emotions) substantially increased the consumption of rewarding but unhealthy snacks, compared to neutral or negative quiz outcomes. The PhD project will investigate how these effects and successive reward-seeking behaviours more generally (e.g., celebrating after positive outcomes) can be explained. A mixed-methods approach will be used in which controlled laboratory tasks, including a small exploratory fMRI paradigm and an ecological field experiment, will be followed by qualitative interviews aimed at helping to interpret the quantitative data. Together, these studies will delineate the factors underlying successive reward engagement by contrasting the roles of (1) bottom-up, neural mechanisms, in which the pre-activation of the brain's reward system primes subsequent reward-seeking, and (2) top-down, cognitive-motivational processes, in which the achievement of positive outcomes is "self-rewarded" to consolidate successful behaviours that led to the outcome. I will further aim to demonstrate that positive outcomes not only increase reward-seeking of food but also engagement with other rewards (e.g., social rewards). The project will offer a novel insight into a uniquely human behaviour - seeking rewards following positive events - but also advance our understanding of every-day behaviours, such as snacking, that are often associated with negative health consequences.