PhD Studentship: Assessing the Extinction Risk and Recovery Potential of Species with Deep Learning (WUT_U23CMP)

University of East Anglia

Deep learning, a powerful class of artificial intelligence (AI) algorithms, is emerging as a promising computational framework for inferring evolutionary signals from highly complicated population datasets [1]. The key challenge here is to design and implement novel deep neuron networks that are capable of detecting the relationships between genomic sequencing data of individuals and associated characteristics of these individuals.

During the past few years, the Wu group have been developing computational methods and tools for inferring evolutionary signals (e.g. recombination and introgression) from genomic datasets (see e.g. [2]). To extend these tools to assessing the extinction risk and recovery potential of species, we will work with Prof. Cock van Oosterhout (School of Environmental Sciences, UEA) to develop AI-based (e.g. deep learning) approach to inferring/detecting evolutionary signals from both genomic and phenotypic datasets. It is expected that these tools will lead to further insights into the understanding of complex evolutionary forces medicating population evolutions, which will be key to understand human population structures, predict pathogen evolution, and design effective conservation policies to mitigate environmental impacts on endangered species [3]. 


  • Korfmann et al. (2023) Deep learning in population genetics. Genome Biology and Evolution.
  • Yuan et al. (2021) Refining models of archaic admixture in Eurasia with ArchaicSeeker 2.0. Nature Communications
  • Bertorelle et al (2022) Genetic load: Genomic estimates and applications in non-model animals. Nature Reviews Genetics
  • Primary supervisor: Taoyang Wu ([email protected] )

    Start date: October 2023

    For more information on this project, please visit

    Entry requirements: Acceptable first degree in Computer Science, Mathematics, Statistics, evolutionary biology, or a related area is expected. An interest in computational biology is helpful, though a prior knowledge of biology is not necessary. Strong programming skills.


    This PhD project is in a competition for studentships allocated to the School of Computing Sciences as a direct result is increased PGT student fee income for the MSc Courses in Cyber Security, Data Science and Computing Sciences.  All successful candidates will be expected to support PGT Lab sessions from October 2023 and related activities as allocated in support of these programmes within the working hours permitted for full-time Postgraduate Researchers.

    Funding comprises ‘home’ tuition fees and an annual stipend (2022/23 rate is £17,668, 2023/24 tbc) for a maximum of 3 years.

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