NOW OPEN: Fully-funded PhD studentship in transport modelling and Mobility as a Service
3 April 2017
UCL-Energy invites applicants for a fully-funded four-year PhD studentship in transport modelling
Supervisors: Dr Maria Kamargianni, Lecturer in Energy & Transport, UCL EI; and Prof. Andreas Schäfer, Professor in Energy & Transport, UCL EI
- Stipend: £16,500 & UK/EU fees, annual research budget of £1000 /yr, and you will also be able to apply for additional funding to UCL schemes to cover extra costs of training and travel.
- Start date: September/October 2017
- Funding duration: 4 years
- Please check eligibility: https://www.epsrc.ac.uk/skills/students/help/eligibility/
The Urban Transport & Energy Group at UCL Energy Institute invites applications for a fully funded four-year PhD studentship covering UK/EU fees plus stipend to focus on the development of the supply components of an advanced transport and energy activity based model able to simulate the multidimensional impacts of new mobility services on travel behaviour, traffic congestion, and energy consumption.
In recent years, new business models, inspired by the sharing economy, and disruptive technologies are ushering in an exciting new age in transportation: the era of Intelligent Mobility. The arrival of ridehailing services, ridesharing, car clubs, community car clubs, and Mobility-as-a-Service (MaaS) are all changing how people get around. Automakers, in turn, increasingly see themselves as both product manufacturers and mobility services providers; in addition to developing next-generation connected and autonomous vehicles, automakers are investing in a wide swath of new mobility services- from car clubs and rental services to multimodal journey planning apps. There is no doubt that consumers have been the primary beneficiaries of new mobility services. However, the impacts of these new mobility schemes and technologies on travel behaviour, traffic congestion, energy use, and emissions are still unclear. Policy makers and urban planners have limited quantified evidence regarding how today’s expanded mobility ecosystem can help advance public policy goals such as reducing GHG emissions and traffic congestion, while providing related benefits such as better air quality.
The proposed PhD topic includes the development of an advanced transport and energy activity based model that will quantify the multiple impacts of new mobility services on travel behaviour, traffic congestion, energy consumption and air quality.
In your PhD you will be expected to master a broad range of theory including Bayesian networks, dynamic traffic assignment, machine learning and big data in order to tackle the mathematical and computational challenges of integrated supply and demand transport and energy activity based models. The project provides an opportunity to conduct cutting edge methodological analysis. During your PhD you will work closely with public transport authorities in London and industry. You will be comfortable with interfacing with professionals from other disciplines and as your PhD unfolds become an expert on integrated transport and energy models applied to the Transport sector.
The project is well-suited to a highly-quantitative individual with strong mathematical, data handling and computing skills. Students should have a bachelor's degree in a relevant subject or a closely-related discipline, awarded with first-class or upper second-class (2:1) honours, or an overseas qualification of an equivalent standard from a recognised higher education institute. For those applicants with a first or 2:1, possession of a master's degree in engineering, computer science, geography or related disciplines is highly desirable. Candidates without a master's degree may be admitted in exceptional cases where suitable research or professional, experience can be demonstrated.
- Excellent analytical and computing skills. Passionate about data analysis, modelling, programming and conducting research.
- A MSc degree in transport engineering, big data analysis, machine learning, software development, geography or other relevant transport or computer science disciplines.
- Candidates without a master's degree may be admitted in exceptional cases where suitable research or professional experience can be demonstrated.
- Knowledge of relevant programming languages or statistical software (such as Python, C++, R, MATLab)
- Ability use own initiative, prioritise workload, and be a fair team player
- Good interpersonal and communication skills (oral and written)
- A high level of attention to detail in working methods
- Interest in the challenges of the Transport sector of the 21st century
Stage 1 - Pre-application documents - (1) CV, (2) academic transcripts, and (3) 1-page personal statement outlining motivation, interest and eligibility for the post - should be emailed directly to Mae Oroszlany: firstname.lastname@example.org.
Stage 2 - Following the interview, the successful candidate will be invited to make a formal application to the UCL Research Degree programme. Further guidance will be provided. http://www.ucl.ac.uk/prospective-students/graduate/research/degrees/energy-mphil-phd
Informal enquiries on the content of the research topic should be emailed to Dr Maria Kamargianni, email@example.com