Improving probabilistic models for automated alignment of Palaeoclimate records
The role will be working on a new project funded by the Isaac Newton Trust about probabilistic algorithms for automated alignment of palaeoclimate records. The post holder will be based in the Department of Geography and will work under the guidance of Dr Francesco Muschitiello.
The stratigraphic correlation of marine sediment cores, speleothems and ice core records plays a central role in palaeoclimate research as it used to develop mutually consistent timescales for climate proxies measured in these archives. To present, the vast majority of stratigraphic correlations are performed manually, which is inherently subjective, often difficult to reproduce and comes without quantification of the confidence of correlations. Alignment algorithms grounded on probability theory can help us address these limitations. In particular, they have an enormous and, as of yet, underexploited potential for automating the correlation of proxy timeseries, ensuring reproducibility and deriving confidence bands associated with the alignment procedure.
This project aims at developing an automated algorithm for stratigraphic correlation and deploying the first graphical user interface (GUI) to perform probabilistic alignment of palaeoclimate records. The successful applicant will: 1) develop an improved Markov Chain Monte Carlo (MCMC) alignment algorithm that models alignments based on multiple proxy signals and incorporates prior knowledge on the depositional history of the climate archives used for correlation; 2) design a dedicated GUI software to facilitate the usability of the algorithm. The new algorithm and related GUI will provide an essential tool to construct robust chronologies for climate archives with poor independent age control and will increase the accessibility of probabilistic alignment methods to the wide palaeoclimate community.
Eligible candidates must have a PhD in Earth Science, Geological Science, Applied Mathematics, Computer Science, or similar. Strong programming skills and prior research experience in MCMC techniques and Bayesian inverse modelling would be a significant advantage. Applicants must also have proven experience of publishing high-quality research articles. They must be highly motivated and should have excellent organisational and communication skills, and be able to work well as part of a team. The successful candidate will be based in Cambridge and have the opportunity to participate in a wide range of departmental and University activities, including the departmental ‘Climate and Environmental Dynamics’ research group, departmental seminars, and reading groups across the University.
Fixed-term: The post is available from 1 April 2022 or as soon as possible thereafter, for a 14-month appointment.
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