or
Looking to list your PhD opportunities? Log in here.
The common hippopotamus (Hippopotamus amphibius) is one of a handful of extant African megaherbivore species. Unlike other megafauna, hippo are relatively understudied. For example the distribution of hippo subpopulations across southern Africa is not well known, and neither is the genetic relatedness between these sub-populations or the extent of isolation. The capacity for hippo dispersal across subpopulations has not been quantified, and the genetic and demographic outcomes of subpopulation connectivity/isolation has not been modelled.
The student will use low-coverage whole genome sequencing to estimate hippo subpopulation structure, reconstruct past population demographic history and employ population models to simulate the population genetic and demographic outcomes of different conservation strategies. Together this will allow us to understand how the current genetic composition of hippo populations has been shaped by environmental processes and anthropogenic factors, and the implications of future hippo conservation actions.
The student will preferably have a background in genetics, genomics and/or bioinformatics. The student will work closely with both Dr Traill (expertise in large mammal ecology and conservation) and Dr Goodman (expertise in population genetics and conservation), and with southern African collaborators. There may be opportunity for field work.
The student will have access to skills development through the YES-DTN, and will be part of two research groups in the School of Biology at Leeds. Academic skills gained will include GIS-based spatial analysis, modeling in R and population genomics.
The minimum entry requirements for PhD study is a 2.1 honours degree, or equivalent, in a subject relating to your proposed area of research, or a good performance in a Masters level course in a relevant subject. A first class honours degree (or equivalent) is usually required to be competitive for scholarship funding and a Masters degree is also a valuable asset.
If English is not your first language, you’ll need to provide evidence of a language qualification. The minimum English language entry requirement for postgraduate research study in the Faculty of Biological Sciences is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.
1) Complete the University of Leeds online application form
Select ‘NERC YES DTN Yorkshire Environmental Sciences’ as the Planned Course of Study.
The supporting documents needed to process your application are:
All documents should be in English or be accompanied by a certified translation into English.
They can be sent via the online research degree application or can be emailed to [Email Address Removed] after you have submitted your application. Your email should include your student ID number (emailed to you on submission of your application), full name and your intended course of study. Please do not send original documents at the application stage and only provide documents via email.
2) Complete the YES.DTN application form. This is available on the YES•DTN website
The Yorkshire Environmental Sciences Doctoral Training Network (YES•DTN) is funded through a BBSRC-NERC Doctoral Landscape Award (DLA) and will recruit up to 26 fully funded PhD candidates per year. For more information, please see: YES•DTN - Yorkshire Environmental Sciences • Doctoral Training Network
Research output data provided by the Research Excellence Framework (REF)
Click here to see the results for all UK universitiesBased on your current searches we recommend the following search filters.
Check out our other PhDs in Leeds, United Kingdom
Start a New search with our database of over 4,000 PhDs
Based on your current search criteria we thought you might be interested in these.
Accounting for remittances in modelling sustainable energy transitions in African cities
University of Southampton
Fractional order modelling of neurons
University of Reading
Ontological modelling for data analysis
Anglia Ruskin University ARU