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Statistical models for digital ecological surveys

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Ecological surveys are the foundation on which evidence-based conservation of the planet’s biodiversity and wildlife resources is built. Wildlife surveys that provide the evidence base have traditionally been conducted by humans and the resulting data treated as “snapshots” in time. However, digital survey devices like camera traps, acoustic arrays or aerial video platforms generate streams of data, not snapshots. These data are more appropriately viewed as time-to-event data, with the events being detections of the species of interest. Using the event times has the potential to yield much richer inferences about the populations under study than snapshot data can, but very little work has been done in this area. If you are interested in developing and applying new statistical methods to exploit the power of digital survey devices, this may be the PhD for you. Specific applications include camera trap surveys of large cats, acoustic surveys of gibbons, chimpanzees, and various bird species, drone-borne aerial surveys of snow leopard prey, and many others.

Funding Notes

Multiple sources of scholarship funding are potentially available, including university, research council (EPSRC) and research group (CREEM). Some are open to international students, some to EU and some UK only.

Applicants should have a good first degree in mathematics, statistics or another discipline (e.g., biology, computer science), with substantial statistical component. A masters-level degree is an advantage.

Further details of the application procedure, including contact details for the Postgraduate Officer, are available at View Website

How good is research at University of St Andrews in Mathematical Sciences?

FTE Category A staff submitted: 30.60

Research output data provided by the Research Excellence Framework (REF)

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