The UCL Energy Institute invites applications for a fully funded 4-year PhD studentship covering UK/EU fees plus stipend to focus on the use of network and graph based methods, informed by big data, to improve energy efficiency. Analysis is to cross multiple disciplines from transport, considering how to optimise public transport networks, to electricity transmission focusing on grid optimisation to reduce CO2 emissions.
- Title: PhD Studentship in Energy Networks and Big Data
- Supervisors: Dr Aidan O’Sullivan, Lecturer in Statistics, UCL-Energy and Professor Tadj Oreszczyn, Director – RCUK Centre for Energy Epidemiology
- Stipend: approx. £16,000 & UK/EU fees
- Start Date: October/November 2016
- Funding Duration: 4 years
- Eligibility please check the EPSRC website
During the PhD you will be expected to master a broad range of theory including Bayesian statistics, graph theory and machine learning in order to tackle the difficult challenge of network optimisation. Additionally we will be interested in the dynamic behaviour of these networks which will require you to work with large datasets of big data to capture accurately and model. The project provides an opportunity to conduct cutting edge methodological analysis working in collaboration with experts from a number of applied fields of research. You will be comfortable with interfacing with professionals from other disciplines and as your PhD unfolds become an in-house expert on big data and network analysis methods applied to the Energy sector.
- Passionate about data analysis, modelling, programming and conducting research
- An MSc degree in statistics, physics, engineering or other data analysis discipline, e.g. machine learning
- Interest in the challenges of the Energy sector of the 21st century
- Knowledge of relevant statistical software or programming languages (such as R, MATLab, Python)
- Ability to use own initiative and prioritise workload
- Good interpersonal and communication skills (oral and written)
- A high level of attention to detail in working methods
For details on how to apply visit the UCL Energy Institute website.