Coventry University (CU) is inviting applications from suitably-qualified graduates for a fully-funded PhD studentship, in the areas of Machine learning, Artificial intelligence, and Environmental modelling.
Responding to risk fluctuations as a result of climate change and assessment of relevant impacts are critical steps toward achieving many of the UN Sustainable Development Goals. This is particularly true in regions of the world where societal and environmental stressors are already impacting communities and ecosystems. Many of such regions can be found along the coastlines, where land is threatened by coastal erosion and flooding driven by storm surges, sea-level rise and intensification of monsoon strength, all of which are associated with climate change. Historically, mangrove forests have mitigated against these hazards, but mangroves have been removed or severely deteriorated due to climate change, human activities and economic pressure.
This project will use cutting edge computational techniques to quantify the historic and potential importance of coastal forests to resilience against environmental hazards in the face of climate change. Central to the project’s objectives will be to construct an efficient, scalable, accurate and computationally-fast methodology based on the latest developments in Bayesian machine learning (ML). The project will provide a platform for an exceptional doctoral candidate to undertake a comprehensive piece of research using state-of-the-art methods, spanning disciplines of data, climate and environmental sciences.
Coventry University has been the UK’s top modern university for seven consecutive years (Guardian University Guide 2013-2019) and holds a number of other prestigious accolades. The PhD will combine the expertise of two of the University’s key Research Centres. The Research Centre for Data Science (CDS) is based in the Faculty of Engineering, Environment and Computing (EEC) of Coventry University. It provides a hub to develop cutting edge research in the areas of Artificial Intelligence, Data Science and Future Computing Technologies. The centre has a vocation to push the boundaries of both fundamental science and practical applications. Established in 2014 through substantial university investment, the Centre for Agroecology, Water & Resilience (CAWR) is rapidly building a global reputation for transdisciplinary research into processes of resilience in social-ecological systems. Among its key lines of research is work focusing on modelling of water and food systems, aided by high performance computing facilities.
Training and Development
The successful candidate will receive comprehensive research training including technical, personal and professional skills.
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.
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.
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)
We are seeking an enthusiastic and highly motivated person with good interpersonal skills and a keen interest in research. You must have at least a 2:1 honours degree or a distinction or high merit at MSc level (or international equivalent) in Mathematics, Statistics, Computer Science, or a related quantitative discipline.
The desired candidate should also have good programming skills in Python, R or Matlab.
For further details see: https://www.coventry.ac.uk/research/research-students/making-an-application/
To find out more about the project please contact Dr Alireza Daneshkhah ([email protected]
). 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.