PhD in Cosmic Landscapes: disambiguating cosmogenic nuclides in detrital populations

University College London

The most common way to estimate the erosion rate at the landscape scale is to calculate the mean residence time of sediment in a river basin by measuring the accumulation of cosmogenic nuclides (rare isotopes formed in the near surface due to the bombardment of cosmic radiation) within the modern river sediment. The low production rate of the commonly-used nuclides (10Be and 26Al) in minerals means analysis of single grains is beyond the detection limit of mass spectrometers. Studies usually amalgamate thousands of mineral grains to yield a measurable signal and constrain a “basin-averaged erosion rate.” However, this amalgamation process means we cannot determine the distribution of nuclide concentrations within a river, diminishing the information contained within the sample and making it impossible to discern whether assumptions are violated during the calculation of erosion rates. This project will work to develop new techniques in sample preparation and analysis to explore the information contained with detrital cosmogenic nuclide concentration populations.

This project will focus on measuring cosmogenic 3He in rocks from the Hawai’ian islands and the Sierra Nevada (California). Laboratory work will take place in the London Geochronology Centre (UCL), SUERC (University of Glasgow), and Group 18 Labs (Arizona State University, USA). These results will be combined with landscape evolution models to better predict how forcing factors such as climate, tectonics, or anthropogenic activity affect erosion rates within changing landscapes. The comparison of model outputs and observations will also provide useful constraints on when it may or may not be appropriate to use basin-averaged techniques.

This project would suit a quantitative scientist with at least a 4-yr degree in geoscience, engineering, or chemistry and a strong interest in geochemistry, mass spectrometry, and landscape evolution who is keen to develop laboratory skills and integrate them with fieldwork and numerical modelling. Although not a prerequisite, experience with programming and statistical languages, Matlab, Python, or C, is desirable. The student will be required to carry out fieldwork in rugged and often remote terranes so independence, physical fitness and an ability to drive will be important.

Application deadline: 18th of June 2023 (including references)

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