The Himalayas represent the largest mountain chain on Earth, and reside mostly in Nepal, India, Pakistan and China. The Himalayas began rising many millions of years ago when India collided with Asia, which changed Earth's climate, altered ocean circulation and chemistry, and impacted the course of biological evolution. Erosion of the Himalayas resulted in deposition of the largest pile of sediment on the planet in the Bay of Bengal, the deep-sea Bengal Fan. Within this sediment record lies the history of the Himalayas - the now eroded Mt. Everests of the past, buried under sediment of the continental shelf and the deepest parts of the Indian Ocean. In 2015, a multi-national expedition on the Joides Resolution, a specially designed drill ship, recovered ~1.5 miles of drill core that contains this record. New research will use sediment from these cores to trace the history of Himalayan erosion and how two of the world's largest rivers, the Ganges and Brahmaputra, delivered this sediment to the Bay of Bengal over the last 3-5 million years. Giant mountain ranges like the Himalayas are a rarity through geologic history, but without the Himalayas there are no drenching Asian monsoons, no fertile floodplains or aquifers, no ancient Indus Civilization, and no Mt. Everests in that part of the world. The results of this research will therefore tell us about climate change, landscape evolution, and how one of the world's most densely populated areas came to be as we see it today. Understanding the past in this way can help us better understand the future for the 10% of the world's population that lives under the influence of this incredible geographic feature.

One of the key benefits of functional programming languages is the ability to reason about programs in a formal manner. However, while the high-level nature of the functional paradigm simplifies reasoning about program correctness, it also makes it more difficult to reason about program efficiency. This reasoning gap is particularly pronounced in lazy languages such as Haskell, where the on-demand nature of evaluation makes reasoning about efficiency even more challenging. We have recently shown how a theory of program improvement can be used to address this problem, demonstrating the feasibility of a unified approach to reasoning that allows both correctness and efficiency to be considered in the same general framework. The aim of this project is to build on the success of this work and develop new high-level techniques for reasoning about functional programs that bridge the correctness/efficiency gap. The project will fund a named researcher for four years, is supported by a fully-funded PhD studentship from the host institution, and is enhanced by a team of leading international collaborators.

Greenland Ice Sheet (GrIS) mass loss acceleration is driven by increasing rates of surface melt and calving of marine-terminating outlet glaciers. The links between increasing surface melt and ice-flow dynamics are poorly understood, in part because we do not mechanistically understand where and under what conditions meltwater accesses the ice-sheet bed at a continental scale. Surface meltwater must reach the ice-bed interface via a surface-to-bed meltwater pathway for meltwater to affect GrIS flow dynamics and, in most cases, for meltwater to contribute to sea level. Surface-to-bed pathways have been manually mapped in local regions (<500 km2), but these methodologies are not practical at the continental scale (~10 to the power of 6 km2). Automated characterization and mapping of ice-sheet surface features is required to fill this gap in knowledge and advance our understanding of the features and processes driving meltwater's influence on ice-sheet dynamics. To understand the formation of surface-to-bed meltwater pathways across the GrIS and their impact on ice-flow dynamics, this three-year project will use a combination of remote-sensing observations, deep learning, and physics-based models to: (1) detect continent-wide surface fractures, moulins and supraglacial lake drainage events with satellite imagery; (2) determine the ice-sheet conditions required to trigger supraglacial lake drainage via hydrofracture and create surface-to-bed pathways; and (3) model the impact of supraglacial lake drainage events on ice-flow dynamics at a regional scale. These objectives will produce the first comprehensive, continental-scale database of GrIS surface-to-bed meltwater pathways and supraglacial lake drainage dates and mechanisms.

The development of radiometric geochronology is one of the greatest triumphs of 20th century geoscience. Geochronology underpins the study of Earth history and puts fundamental constraints on the rate of biological evolution. Tremendous resources are invested in the development of sophisticated mass spectrometers capable of measuring isotopic ratios with ever increasing resolution and sensitivity. Unfortunately, the statistical treatment of mass spectrometer data has not kept up with these hardware developments and this undermines the reliability of radiometric geochronology. This proposal aims to create a 'software revolution' in geochronology, by building an internally consistent ecosystem of computer programs to account for inter-sample error correlations. These have a first order effect on the precision and accuracy of geochronology but are largely ignored by current geochronological data processing protocols. The proposed software will modify existing data reduction platforms and create entirely new ones. It will implement a data exchange format to combine datasets from multiple chronometers together whilst keeping track of the correlated uncertainties between them. The new algorithms will be applied to five important geological problems. 1. The age of the Solar System is presently constrained to 4567.30 +/- 0.16 Ma using primitive meteorites. The meteorite data are 'underdispersed' with respect to the analytical uncertainties. The presence of strong inter-sample error correlations is one likely culprit for this underdispersion. Accounting for these correlations will significantly improve the accuracy and precision of this iconic age estimate. 2. The Cretaceous-Palaeogene boundary marks the disappearance of the dinosaurs in the most notorious mass extinction of Earth history. We will re-evaluate the timing of critical events around this boundary using high precision 40Ar/39Ar geochronology. Preliminary results from other samples show that 40Ar/39Ar data are prone to strong (r^2 > 0.9) inter-sample error correlations, and that these have a first order effect on the precision and accuracy of weighted mean age estimates. A sensitivity test indicates that this may change the timing of the mass extinction by up to 200ka. 3. The 'Taung Child' is a famous hominin fossil that was discovered in a South African cave in 1924. It is considered to be the world's first Australopithecine, but has not yet been dated. We have a good unpublished U-Pb age of 1.99 +/- 0.05 Ma from a tufa collected above the hominid, and an imprecise upper age limit of 1.4 +/- 2.7 Ma on a calcrete deposit below it. Applying the new algorithms to the latter date will greatly improve its precision. This will be further improved with additional measurements, in time for the 100th anniversary of the Taung Child's discovery. 4. Depth profiling of the U-Pb ages in rutile and apatite provides an exciting new way to constrain the thermal evolution of lower crustal rocks. However, the laser ablation data used for this research are prone to strong error correlations that are not accounted for by current data reduction protocols. These protocols will be revised using the new software, permitting better resolution of the inferred t-T paths. (5) Radiogenic noble gases such as 40Ar (from 40K), 4He (from U, Th and Sm), and 129Xe (from 129I) are lost by volume diffusion at high temperatures. The revised regression algorithms implemented by the research programme will be applied to step-heating 'Arrhenius' experiments. This will improve the calculation of diffusion coefficients for these gas species, resulting in further improvement of (noble gas) thermochronology.

The reliance of military systems and armed forces on the EM spectrum creates vulnerabilities and opportunities for electronic warfare (EW) in support of military operations. EW is concerned with detecting, recognising then exploiting and countering the enemy's electronic order of battle, and calls for the development of innovative algorithmic solutions for information extraction and delivery of signals in contested electromagnetic environment. Traditionally, the subject of signal sensing/information extraction has been developed separately from the area of signal delivery. In contrast, this visionary project conducted at Imperial College London and University College London aims at leveraging the consortium complementary expertise in various areas of signal processing (sparsity, super-resolution and subspace methods, communications, radar, and machine learning) for civilian and defence applications to design and develop novel and innovative solutions for a cohesive treatment of information extraction and delivery of signals in contested electromagnetic environment. To put together this novel approach in a credible fashion, this project is organized in two major work packages. The first work package will analyze, separate and characterize signals across time, frequency, and space and extract useful information from those signals by developing and leveraging novel super-resolution, subspace and deep learning methods. The second work package will leverage progress made in the first work package and design signals and system responses for sensing and signaling in congested RF environments. Novel waveform design approaches will be derived for sensing using an extended ambiguity function-based framework, for precise spatiotemporal energy delivery using network-wide time-reversal and for joint sensing and signaling. Attention will also be drawn to the design of signals resilient to hardware and nonlinear channel responses. The project will be performed in partnership with academia/research institutes (University of Kansas, Fraunhofer) and industrial leaders in civilian and military equipment design and manufacturing (IBM, US Army Research Lab, Thales). The project demands a strong track record in a wide range of signal processing techniques and it is to be conducted by a unique research consortium with a right mix of theoretical and practical skills. With the above and given the novelty and originality of the topic, the research outcomes will be of considerable value to transform the future of electronic warfare and give the industry and defence a fresh and timely insight into the development of signal processing for contested electromagnetic environment, advancing UK's research profile in the world. Its success would radically change the design of electronic support measures, electronic coutermeasures and electronic counter-coutermeasures and have a tremendous impact on the defence sector and industry.