Applications are invited for fully-funded PhD studentships at Lancaster
<br>University in which you will learn to develop cutting-edge data science
<br>approaches to address key environmental science challenges.
<br>
<br>-------------
<br>Description of the PhD:
<br>
<br>Decision Making in the Face of Uncertainty: A Qualitative Study
<br>
<br>Supervisors: Bran Knowles and Gordon Blair
<br>
<br>The DSNE Project is seeking to address three grand challenges of the
<br>natural environment through the development of novel data science
<br>methods, including a combination of machine learning and statistical
<br>techniques all within a Bayesian framework. The aim is to support
<br>decision making under uncertainty and to derive techniques which both
<br>quantify and constrain this uncertainty, arguably the holy grail of
<br>environmental science. In the project, we are working closely with a
<br>range of stakeholders and it is important to understand the how
<br>stakeholders derive valid and actionable conclusions from the underlying
<br>data science. To identify strategies for cultivating such
<br>understandings, qualitative research is needed about the basis for
<br>assumptions used by stakeholders in interpreting uncertainty and what
<br>factors influence these assumptions. Through ethnographic study of the
<br>three grand challenges being undertaken as part of DSNE, this
<br>studentship will investigate stakeholders’ evolving conceptions of
<br>uncertainty over a period of engagement with data scientists. The
<br>successful student will develop their own research questions, but we
<br>expect a multidisciplinary approach drawing on theories of uncertainty,
<br>with investigations including how stakeholders think about, handle, or
<br>even make use of uncertainty in data and data science.
<br>
<br>-------------
<br>
<br>About the PhD studentships:
<br>The studentships are part of the large-scale £2.6M EPSRC-funded grant
<br>Data Science for the Natural Environment (DSNE), a joint project between
<br>Lancaster University and the NERC Centre for Ecology & Hydrology (CEH).
<br>This is an exciting opportunity to work at the heart of a
<br>multi-disciplinary team of researchers consisting of computer
<br>scientists, statisticians, environmental scientists and stakeholder
<br>organisations, working together to deliver methodological innovation in
<br>data science to tackle grand challenges around environmental change.
<br>
<br>If learning to develop and deploy data science techniques to solve the
<br>biggest problems faced by humanity is an appealing next step, please
<br>send a letter of application to dsne@lancaster.ac.uk by 5pm on Monday
<br>11th February. Unfortunately, while we can cover the full fees and
<br>stipend of UK/EU applicants, the full fee of non-EU applicants cannot be
<br>covered. The letter should include:
<br>
<br>1. An ordered list of which of the PhD projects you would like to be
<br>considered for, with an explanation of your reasoning
<br>2. An explanation of why your skill set and previous education will
<br>allow you to be successful in these projects (a transcript of your
<br>undergraduate or masters degree programme is likely to be helpful).
<br>
<br>To be able to answer these questions sensibly, it is advisable to talk
<br>to the supervisors of your desired projects, in advance of submitting
<br>your application.
<br>
<br>Unfortunately, while we can cover the full fees and stipend of UK/EU
<br>applicants, the full fee of non-EU applicants cannot be covered.
<br>
<br>Extended submission deadline is the 12th of April, 2019.
<br>
<br>PhD topics are available across the spectrum of DSNE research, including
<br>social science aspects, for more information please visit:
<br>
<br>https://www.lancaster.ac.uk/data-science-of-the-natural-environment/studentships/#understanding-trust-in-environmental-data-science:-cross-disciplines-and-cross-cultures
<br>
<br>For inquires about the advertised PhD projects, please contact Dr Bran
<br>Knowles:
<br>
<br>b.h.knowles1@lancaster.ac.uk
<br>https://www.lancaster.ac.uk/people-profiles/bran-knowles
<br>
<br>
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