or
Looking to list your PhD opportunities? Log in here.
Supervisors
Dr Dezong Zhao is a Reader in Autonomous Systems and Connectivity at The University of Glasgow.
Prof Lei Zhang is a Professor of Trustworthy Systems at The University of Glasgow.
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: You will spend the first year of the CDT at the University of Manchester undertaking taught MSc studies and research training. You must achieve an average of 65% or higher in your MSc taught 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 Communication and Signal Processing
Year 2 – 4 PhD Location: University of Glasgow
University-of-glasgow-logo-gla | PROPHESEE
Research Abstract: This research aims to enable robots to tackle physical-interaction-rick tasks in constrained environments. By utilising recent advances in visual language models (VLM), robots are expected to be capable of reasoning, planning and manipulations for complex tasks. The tasks will first be decomposed into sub-tasks and then refined through causal reasoning. VLMs will enhance perception and planning, enabling robots to interpret the environment and generate physically feasible motion references. Reinforcement learning allows robots to learn control strategies. This dynamic framework surpasses traditional sense-plan-act pipelines, empowering robots to proactively adapt to complex, unstructured environments, enhancing their ability to handle unpredictable real-world tasks.
For a group of robots, a promising solution is utilising distributed mechanisms to achieve trustworthy joint decisions. The current solutions are mainly running in centralised manner, suffering from privacy, scalability and single point of failure issues. Distributed solutions leverage the fault tolerance of distributed systems. A novel distributed approach is to be realised in achieving ultra-reliable and fault-tolerant consensus for connected robots. The approach provides trustworthy and low-cost solutions for wireless connected robots. We will jointly optimise the consensus and communication network to meet the ultra-reliable, real-time and high throughput requirements.
Eligibility
Applicants should have a First or strong Upper Second-class honours degree (2:1 with 65% average), or international equivalent, in Engineering with evidence of previous study in communication and signal processing engineering fundamentals.
Funding
Please see funding notes.
Funding for this RAINZ studentship is provided by EPSRC and Magnox (NRS).
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:
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).
This 4 year studentship will cover full tuition fees at the Home student rate and a maintenance grant, starting at the UKRI minimum of £19,237 pa (to increase with each year). 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.
The university will respond to you directly. You will have a FindAPhD account to view your sent enquiries and receive email alerts with new PhD opportunities and guidance to help you choose the right programme.
Log in to save time sending your enquiry and view previously sent enquiries
The information you submit to University of Glasgow will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Glasgow, United Kingdom
Start a New search with our database of over 4,000 PhDs
Based on your current search criteria we thought you might be interested in these.
Microfluidic-assisted particle manipulation in complex fluid environments for biomedical and engineering applications
University of Strathclyde
Integrating machine-learning techniques to learn interactions between structural components and physical domains
University of Sheffield
Distributed acoustic sensing for monitoring complex environments
University of Southampton