The dhole (Cuon alpinus, Pallas 1811) is a medium-sized evolutionary distinct canid, currently distributed across Southeast Asia. Despite being threatened by prey depletion, habitat destruction and competition, very little is known about the dhole's distribution and ecology. Dhole-specific conservation strategies are inadequate, if not entirely absent in all range countries. Populations reside mainly in protected areas for other charismatic species, but their adequacy for dhole conservation is undetermined. Species distribution models (SDMs) that relate georeferenced occurrence records to environmental variables could be used to identify suitable dhole conservation strategies. However, modern dhole distribution data is sparse and heavily influenced by human interactions. This could introduce bias into model projections. Incorporating the dholes Pleistocene fossil record into models could reduce the effects of environmental truncation and broaden understanding of the dholes' ecological niche. However, bias in the dhole's fossil records may increase uncertainly into model outputs. This PhD project aims to address these issues by (1) producing a robust and quality assured chronology of the dhole's distribution from the Middle Pleistocene onwards, (2) quantifying the uncertainty in the dholes modern and fossil records using Bayesian approaches, and (3) assessing the contribution of fossil records to dhole ecology and conservation.

The research explores how technology hardware, such as sensors, are integrated into public places in the UK. A socio-technical approach drawing on multiple disciplines and qualitative methods, such as interviews, is used. Through mapping the actors and processes involved in the integration of technology hardware in public places, factors shaping digital security are investigated. Furthermore, the role of narratives is explored to understand how these may shape digital security outcomes. The overarching aim of the research is to understand how digital security could be improved in this context.

Scalable machine learning of complex models on extreme data will be an important industrial application of exascale computers. In this project, we take the example of predicting compound bioactivity for the pharmaceutical industry, an important sector for Europe for employment, income, and solving the problems of an ageing society. Small scale approaches to machine learning have already been trialed and show great promise to reduce empirical testing costs by acting as a virtual screen to filter out tests unlikely to work. However, it is not yet possible to use all available data to make the best possible models, as algorithms (and their implementations) capable of learning the best models do not scale to such sizes and heterogeneity of input data. There are also further challenges including imbalanced data, confidence estimation, data standards model quality and feature diversity. The ExCAPE project aims to solve these problems by producing state of the art scalable algorithms and implementations thereof suitable for running on future Exascale machines. These approaches will scale programs for complex pharmaceutical workloads to input data sets at industry scale. The programs will be targeted at exascale platforms by using a mix of HPC programming techniques, advanced platform simulation for tuning and and suitable accelerators.