Tips on how to manage your PhD stipend FIND OUT MORE
University of Hong Kong Featured PhD Programmes
University of West London Featured PhD Programmes
University of Hull Featured PhD Programmes

PhD studentship: Assured and Scalable Self-Adaptation for the Engineering of Trustworthy Autonomous Robotic Teams


Department of Computer Science

About the Project

Applications are invited for a three-year PhD studentship, supported by the Defence Science & Technology Laboratory (DSTL), to be undertaken within the Department of Computer Science (http://www.cs.york.ac.uk/) at the University of York. The successful applicant will join one of UK’s top research departments in Computer Science, and will pursue research in the rapidly evolving areas of machine learning and formal methods for safety-critical systems, under the supervision of Dr. Simos Gerasimou and Dr. Javier Camara Moreno. The PhD project will include secondments at École Nationale Supérieure de Techniques Avancées, ParisTech in Paris, France, where the PhD student will be co-supervised by Dr Natalia Díaz Rodríguez.

Background of the Project

Robotic teams and other distributed autonomous systems (DAS) are increasingly used in several application domains including logistics, manufacturing, and infrastructure inspection. Assuring the trustworthiness of DAS carrying out a safety-critical task collaboratively is very challenging due to uncertainties and risks associated with the operating environment, team member failures, etc. To overcome these challenges, the distributed-control software of DAS should exhibit high levels of scalability and optimality underpinned by assurance evidence that the DAS operates safely in continually changing and unexpected scenarios. The PhD project will contribute significantly to addressing this challenge by leveraging the capabilities from both data-driven (using machine learning) and model-based (using formal methods) paradigms to devise assured and scalable self-adaptation techniques that support the development of trustworthy distributed-control software for DAS. These techniques will be integrated with state-of-the-art DAS middleware and their feasibility will be validated through a demonstrator both in simulation and using mobile robots available in our lab.

Research supervision

If successful, you will conduct your research under the co-supervision of:

Dr Simos Gerasimou - https://www-users.cs.york.ac.uk/simos/

Dr Javier Camara Moreno - https://www-users.york.ac.uk/~jcm567/index.shtml

Dr Natalia Díaz Rodríguez - https://nataliadiaz.github.io

To apply for this studentship

You must apply online for a full-time PhD in Computer Science (https://www.cs.york.ac.uk/postgraduate/research-degrees/phd/#tab-4)

You must state “ASSA DSTL Studentship” in the “Funding information” section of your application.

There is no need to write a formal research proposal in your application to study as this studentship is for a specific PhD project. However, a research statement elaborating your interest in the project and how your skills match the project requirements is desirable.

We will look favourably on applicants that can demonstrate a strong mathematical background, a keen interest in machine learning and AI, and excellent writing, communication, presentation and organization skills.

This project is being interviewed for and filled on a rolling basis. Please submit your application at your earliest opportunity.


Funding Notes

To be considered for this funding you must:

meet the entrance requirements for a PhD in Computer Science
be a UK or French citizen

If successful, you will be supported for three years. Funding includes:

£15,285 (2020/21 rate) per year stipend
Home/EU tuition fees
Provision for secondments to ENSTA Paris, research collaboration visits, and travel to conferences.

References

Project enquiries
Dr Simos Gerasimou
simos.gerasimou@york.ac.uk

Dr Javier Camara Moreno
javier.camaramoreno@york.ac.uk

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here

The information you submit to University of York 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.

* required field

Your enquiry has been emailed successfully



Search Suggestions

Search Suggestions

Based on your current searches we recommend the following search filters.



FindAPhD. Copyright 2005-2021
All rights reserved.