Project Description
Supervisors
Professor Cock van Oosterhout, School of Environmental Sciences, University of East Anglia
Professor Jim Groombridge, Durrell Institute of Conservation and Ecology, University of Kent
Professor David Richardson, School of Biological Sciences, University of East Anglia
Dr Herán Morales, Globe Institute, University of Copenhagen
Scientific Background
Population bottlenecks erode genetic variation, which makes threatened species susceptible to viral epidemics. So, what makes an individual Resistant, Tolerant, or Susceptible (R/T/S) to a virus? That is the key question we aim to answer in this PhD study. We have gathered data on viral outbreaks over ~20 years and sequenced hundreds of bird genomes, working on some of the best-studied avian models (Mauritius parakeet, ringneck parakeet, red-crowned parakeet, and orange-bellied parrot). The PhD candidate will study data from the Mauritius parakeet to develop an AI model that can predict the response to viral infection based on genomics data. Moreover, there is the option to conduct fieldwork in Mauritius to gather additional field data, and for comparative genomics analyses.
Research Methodology
During this PhD project, we will sequence genomes of 531 already-sampled full-sib pairs with known R/T/S status. (This is funded by recently awarded NERC grant). The PGR will conduct bioinformatic analyses on these data, focussing on immune genes, and perform a comparative genomic analysis across parakeets and parrots. They will construct an AI model to identify genomic predictors of R/T/S in the Mauritius parakeet, and they will conduct analysis in SLiM to examine how genomic erosion reduces immunogenetic diversity, and how that affects disease susceptibility. The PGR will test the following hypotheses:
- Genome erosion increases susceptibility to viral infection.
- Inbred individuals are more susceptible to viral infection.
- Individuals with Runs of Homozygosity (ROH) spanning immune genes are more susceptible to viral infection.
- Differences in alleles at immune genes explain variation in disease status of birds.
Objectives
Develop a multispectral imaging dataset of marine litter materials by extending the existing VL dataset.
Design and evaluate DL models capable of classifying marine litter types using multispectral data, with a focus on achieving robustness to varying spectral channel configurations and illumination conditions.
Implement and validate device-independent representations. Investigate and apply domain adaptation and transfer learning techniques to develop models that generalize across different imaging devices, including future sensors with unknown spectral sensitivities.
Training
AI modelling, SLiM modelling (i.e., an individual based model to assess population viability), genomics analysis, immune gene analysis, applied bird conservation, comparative genomics, and tropical field work (optional).
Person Specification
Prior experience in computer coding (e.g., Python, SLiM), AI modelling, and understanding of evolutionary or conservation genetics / genomics is desirable. Good teamwork skills are essential.
Acceptable first degree subjects: Evolutionary Biology, Genetics, Conservation Biology
Project code: VANOOSTHERHOUT_UEA_ARIES26_CASE
All ARIES CASE studentships include a three to 18-month placement with the non-academic CASE partner during their period of study. The placement offers experience designed to enhance professional development.