About the Project
Low-cost wireless networkable sensors that can gather data about environments, human behaviours and interactions between people and common life objects are widely available. Dedicated networks can be built to collect detailed data over long periods of time.
Much of our understanding about human behaviour has historically come from surveys, interviews, focus groups and ethnographic studies. New methodologies that correctly mine diverse, mixed datasets (sensor based, survey-based and qualitative) are critical to enhance our understanding of human behaviour, needs and aspirations, and drive decisions in response to future development challenges.
The project will look at the challenges and opportunities of inferring knowledge from and/or making decisions based on diverse datasets such as those gathered from and about energy systems and will innovate in the new area of human sensors.
Coventry University
Coventry University (CU) is inviting applications from suitably-qualified graduates for a fully-funded PhD studentship (part of the CU Global Challenges Research programme) working to understand energy needs and providing new technical solutions for displaced populations in Rwanda and Nepal.
This Studentship will be based within an EPSRC-funded project called HEED-Refugee. The successful applicant will be working with energy survey data collected by the project team as well as data that is streamed from hundreds of wireless sensors embedded in deployed energy interventions (such as mobile lanterns, cookstoves, street lights and microgrids in the camps) in order to perform real-life evaluation of the innovations that support refugees.
There is a wide availability of low-cost wireless networkable sensors that can gather data about environments, human behaviours and interactions between people and common life objects. Dedicated networks can be built to collect detailed data over long periods of time. However, much of our understanding about human behaviour has come from surveys, interviews, focus groups and ethnographic studies. New methodologies that correctly analyse diverse, mixed datasets (sensor based and survey based) are critical to enhance our understanding of human behaviour, needs and aspirations, and drive decisions in response to a number of development challenges.
You will work within an interdisciplinary team of scientists, project collaborators (including Practical Action, NGO and Scene - a social IT enterprise pioneering solutions in the energy sector) and fellow doctoral researchers
Benefits
Training and Development
The successful candidate will receive comprehensive research training including technical, personal and professional skills, and will have the opportunity to undertake field work in refugee camps in Rwanda and Nepal.
All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.
Candidate Specification
Entry criteria for applicants to PHD
• A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
PLUS
the potential to engage in innovative research and to complete the PhD within a 3.5 years
• a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)
For further details see: https://www.coventry.ac.uk/research/research-students/making-an-application/
• Relevant degree in Mathematics, Computer Science disciplines or Social sciences
• A desire and ability to work across disciplines and in an interdisciplinary subject area focusing on data and mixed research methodologies - essential
• Programming skills in a language/tool of choice---essential
• Strong Mathematics or Statistics background---essential
• Demonstrable experience with data analysis and data processing principles and tools---essential
• The desire to participate in field work---essential
• Working knowledge of machine learning tools and techniques ---desirable
How to Apply
To find out more about the project please contact Professor Elena Gaura [Email Address Removed].
To apply on line please visit: https://pgrplus.coventry.ac.uk/
All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.
Continue with Facebook
