Looking to list your PhD opportunities? Log in here.
This project is no longer listed on FindAPhD.com and may not be available.
Click here to search FindAPhD.com for PhD studentship opportunitiesAbout the Project
This project is part of the DPhil in Biology at the University of Oxford
Over the last three years, we have brought together a large collaboration of researchers to study UK seals using drones and time-lapse cameras. In this project, the student will build on this network to develop structured demographic models and in turn use these models to answer questions about the resilience of UK seal populations to climate change, response to local disturbance, and exploitation or competition with fisheries.
We need a highly motivated PhD student for a challenging but important and ultimately rewarding study that would equip the student with good skills in modern conservation ecology for either academia or industry. This project would suit someone from actuarial sciences (physics and mathematics) that is prepared to get muddy, or a biologist that was already coding to an advanced level. If you are interested but unsure, please contact us to discuss before you rule yourself out - we acknowledge that no one candidate has all the skills we need and we will put in place a training plan depending on the successful candidate.
You will need to be able to conduct fieldwork around the UK on seal populations using drones, process these into standard image based surveys, and then extract data and process into structured demographic models.
Skills required:
- Competency in coding (R/Python) and statistics, with a high degree of problem solving (essential)
- Excellent people skills (essential)
- Drone flying (essential, but we will train you if needed)
- GIS (basic)
- Ecology background (desirable)
- To be competitive for DTP studentships, a previous publication record is highly advantageous
This project is part of the Ecology & Conservation theme in the Department of Biology.
Funding
This project is part of the DPhil in Biology programme, and is not a funded course at the University of Oxford, as such, students are expected to explore options for funding. However, we anticipate being able to offer around 6 full graduate scholarships to incoming DPhil Students in 2023-24
You will be automatically considered for the majority of Oxford scholarships, if you fulfil the eligibility criteria and submit your graduate application by 20 January 2023. Scholarships are awarded on the basis of academic achievement and potential to excel as a DPhil student.
For further details about searching for funding as a graduate student visit the University’s dedicated Funding pages.
Eligibility
For full entry requirements and eligibility information, please see the main admissions page.
How to apply
The deadline for applications for 2023-2024 entry is midday 20 January 2023. We will continue to accept applications submitted after 20 January 2023, but these late applications will not be considered for scholarship funding.
You can find the admissions portal and further information about eligibility and the DPhil in Biology Programme at the University's graduate admissions page.
References
2. Weinstein, B.G., …., Frederick, P., Ernest, S.K.M., A general deep learning model for bird detection in high-resolution airborne imagery. Ecological Applications n/a, e2694.
How good is research at University of Oxford in Biological Sciences?
Research output data provided by the Research Excellence Framework (REF)
Click here to see the results for all UK universities
Search suggestions
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Oxford, United Kingdom
Check out our other PhDs in United Kingdom
Start a New search with our database of over 4,000 PhDs

PhD suggestions
Based on your current search criteria we thought you might be interested in these.
SUPER DTP: Further development of the ECOSSE-model for quantifying and mapping the impacts of degraded and restored Scottish peatlands on net greenhouse gas (GHG) emissions under climate change
Aberdeen University
The effects of climate change on the performance of geo-infrastructure under cyclic loading
University of Portsmouth
AI to the rescue of climate change, modelling air quality for cleaner urban planning
Anglia Ruskin University ARU