Planning for Robust Action for Autonomous Vehicles Under Epistemic Uncertainty

This studentship is offered by the EPSRC Centre for Doctoral Training in Robotics and Artificial Intelligence for Net Zero (RAINZ CDT) which is a partnership between three of the UKs leading universities (University of Manchester, University of Glasgow and University of Oxford).

Robotics and Autonomous Systems (RAS) is an essential enabling technology for the Net Zero transition in the UK’s energy sector. However, significant technological and cultural barriers are limiting its effectiveness. Overcoming these barriers is a key target of this CDT. The focus of the CDT’s research projects will be how RAS can be used for the inspection, maintenance, and repair of new infrastructure in renewables (wind, solar, geothermal, tidal, hydrogen) and nuclear (fission and fusion), and to support the decarbonization of existing maintenance and decommissioning of assets.

We are seeking ambitious graduate scientists and engineers who are keen to acquire new skills and have a desire to help increase use of RAS to help decarbonise the energy sector. You will become a pioneer and leader in this increasingly important area of science and engineering.

RAINZ_CDT

Year 1: All students in the CDT will spend the first year at the University of Manchester undertaking taught MSc studies and research training. Students must achieve an average of 65% or higher in their MSc assessments to be considered for progression to the PhD studies.

Note: you will not graduate with an MSc. If you meet the progression criteria, you will transition directly onto the PhD.

Years 2 – 4: You will move to your host institute (University of Manchester, University of Glasgow, or University of Oxford) to undertake your PhD research, which will be complimented with a comprehensive cohort training and employability development programme.

About this Project

Year 1 MSc Course: MSc Robotics

Year 2 – 4 PhD Location: University of Oxford

Research Abstract: Autonomous Vehicles (AVs) rely on modules for perception, prediction, and localisation in order to operate reliably in dynamic, safety-critical settings such as motorway driving. The performance of these modules is typically known to be good within an operational design domain (ODD), which may be based on prior assumptions, training data coverage, or other constraints. This leads to the question of how should an AV behave when it must act on the boundary or beyond its ODD.

In this PhD you will model the operation of an AV beyond its ODD using approaches from stochastic control and planning under uncertainty. You will explore techniques from robust planning/optimisation under epistemic uncertainty to generate safe behaviours when an AVs processes are performing unreliably, e.g. due to degraded sensors, unusual environmental conditions, or software/hardware failures.

Eligibility

Applicants should have a First or strong Upper Second-class honours degree (2:1 with 65% average), or international equivalent, in Engineering, Computer Science, Physics or Mathematics with evidence of programming experience.

Funding

The studentship will cover full tuition fees at the Home student rate and a maintenance grant for 4 years, starting at the UKRI minimum of £19,237 pa for 2025-2026. The Studentship also comes with access to additional funding in the form of a research training support grant which is available to fund conference attendance, fieldwork, secondments, etc…

International applicants are welcome, although only Home student rates will be funded. The difference between International student rates and Home student rates needs to be covered through alternative funding sources, and we encourage all international applicants to consider this when applying.

Funding for this RAINZ studentship is provided by EPSRC and Oxa.

This project is subject to funding being confirmed by the industry partner.

Before you apply

We strongly recommend that you contact the supervisors for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

How to apply

Applications should be made through the RAINZ CDT website: www.rainz-cdt.ac.uk, where you can also find further information about the CDT. Informal enquiries can be made by emailing rainz@manchester.ac.uk. 

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

The application deadline is 17:00, 28th November 2024. Applications received after this time will not be considered.

Equality, diversity and inclusion is fundamental to the success of RAINZ CDT, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).