PhD Studentship: Learning for Control: Enabling Efficient Networked Autonomous Systems

Project title: Learning for Control: Enabling Efficient Networked Autonomous Systems  

Supervisory Team: Konstantinos Gatsis 

Project description:

There is an ongoing transformation in engineering autonomous systems that aim to achieve complex objectives with limited human intervention in applications such as robotics, self-driving cars, and industrial systems. While the design of autonomous systems has typically relied on pre-defined models, the desire to operate in complex, unknown, or varying conditions implies that models of the system and the operating environment may not be always available. As a result, machine learning and data-driven approaches are on the rise and have the potential for impact in autonomous systems. However, embedding machine learning in autonomous systems is facing significant challenges in terms of safety, robustness, and resource efficiency. 

In this project, we focus on the problem of designing control policies for autonomous systems. We aim to develop new methodologies that combine control, optimization, and machine learning tools. The objectives are to a) consider resource constraints: limited computing to run complex machine learning models for example neural networks, or limited communication resources in networked autonomous systems for example in a network of robots, b) analyze the safety and robustness of the learned policies to disturbances or malicious disruptions, c) demonstrate the benefits of the design methodologies in numerical simulations. Experimental evaluation may also be considered. 

You will have a background in control systems, mathematical optimization, and machine learning, as well as excellent numerical analysis and programming skills. 

You will gain training in advanced mathematical and numerical skills. We will support the advancement of your career and provide opportunities for professional networking and external collaborations. 

You will be based in the Cyber Physical Systems group with academics and researchers in the broad areas of computer engineering, embedded systems, control systems, and formal methods. The group is in the School of Electronics and Computer Science, which is highly ranked in the UK and worldwide in electrical engineering. 

Related references by the supervisor 

Neural-network-based state estimators 

Communication-efficient Reinforcement Learning

Learning for Networked Control Systems 

Before you apply 

Enquiries regarding this project can be sent to Dr Konstantinos Gatsis at k.gatsis@soton.ac.uk  

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent). 

Closing date: 31 August 2024.  Applications will be considered in the order that they are received, the position will be considered filled when a suitable candidate has been identified. 

Funding: We offer a range of funding opportunities for both UK and international students, including Bursaries and Scholarships. For more information please visit PhD Scholarships | Doctoral College | University of Southampton  Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered. 

How To Apply

You should submit your completed online application form by clicking the ‘Apply’ button above. Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Elect & Elect Eng (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Konstantinos Gatsis 

Applications should include:

• Research Proposal
• Curriculum Vitae
• Two reference letters
• Degree Transcripts/Certificates to date 

For further information please contact: feps-pgr-apply@soton.ac.uk 

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