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PhD Opportunities at CRADLE Robotics and AI

The Centre for Robotic Autonomy in Demanding and Long-lasting Environments (CRADLE) is an EPSRC-funded Prosperity Partnership that brings together the industrial experience that Jacobs has in applied robotics and autonomous systems (RAS) with the research expertise at the University of Manchester in this field. The partnership aims to deliver novel and transformational RAS technologies that enable robots to be deployed in the most demanding environments, such as space, nuclear, energy generation, and urban infrastructure, over long lengths of time.

To support CRADLE, we aim to admit and fund a cohort of PhD students, who will complete research projects linked to various aspects of the programme.

Specific research topics are given as follows (click to reveal description):

  • PhD Project 1: End-to-end Methods for the Verification of Autonomous Planetary Deployments 

    UoM Supervisory Team: Dr Marie Farrell, Dr Louise Dennis, Prof Michael Fisher (Computer Science)

    Industry Supervisor: John Brotherhood (Jacobs)

    This project seeks to develop, and exhibit, end-to-end methods for the heterogeneous verification of autonomous systems in (practical) planetary deployments. It will address how to integrate results from heterogeneous verification efforts that focus on individual system modules, investigating how evidence can be combined to support traceability from requirements/system-level properties to verification conditions/artefacts and final implementation.

  • PhD Project 2: Formal Verification of Robot Teams or Human-Robot Interaction

    UoM Supervisory Team: Prof Clare Dixon, Prof Michael Fisher (Computer Science)

    Industry Supervisor: John Brotherhood (Jacobs)

    Robots are developed to work in small groups (robot teams) or may be designed to work in collaboration with humans (human robot interaction). The former might be necessary as it may take several robots at achieve some task, some to hold an item whilst others work on it, several to transport something etc. For the latter, robot assistants need to interact closely with the people they are working with.

    It is important to make sure both robot-robot teams and human-robot teams can achieve the task where possible, can recover from failure, interact in a safe manner etc. Verification is the process that checks the system does satisfy these required properties. This is often carried out using simulation or real robot experiments. Formal verification is a mathematical analysis of systems using techniques such as model checking or temporal theorem proving.

    This project involves developing and applying formal verification to robot teams and human robot interactions. Additionally, using different formal and informal verification techniques together to improve confidence in systems could be further investigated. This will help ensure that they satisfy the required properties and to investigate the robustness of the system to failure.
  • PhD Project 3: Python Program Model-Checking/Verifying Python Agents

    UoM Supervisory Team: Dr Louise Dennis (Computer Science)

    Industry Supervisor: John Brotherhood (Jacobs)

    Overview: Many in the Robotics community are moving away from C, C++, Java towards Python, with its small footprint and ease of use. However, we do not yet have a good way to verify Python programs; we only have testing, which gives quite weak results in more complex environments. To provide much greater confidence in Python components, we require a formal verification tool for Python, ideally one that verifies Python code directly, rather than model of expected behaviour.

    Theoretical: The aim of this project is to explore the potential formal verification of Python and, if appropriate, the use of Java PathFinder-like technology for Python. Work on introducing and developing Java PathFinder has shown how program model-checking can become an effective tool for software engineering and system assurance. We will also explore other options to formal Python verification.

    Practical: Utilising the Java PathFinder approach will involve developing a backtracking Python interpreter either based on an existing Python virtual machine or on the (formal) operational semantics for Python. This backtracking machine will then be augmented by mechanisms to recognise repeating states and deadlocks, as in the “on the fly model-checking” approach.

    Application: Once working viably, then extension to BDIPython would allow for the verification of more complex but transparent autonomous components.
  • PhD Project 4: Formal Verification of Robot Swarms

    UoM Supervisory Team: Prof Clare Dixon, Prof Michael Fisher (Computer Science)

    Industry Supervisor: John Brotherhood (Jacobs)

    A robot swarm is a collection of simple robots designed to work together to carry out some task. Such swarms rely on the simplicity of the individual robots, the fault tolerance inherent in having a large population of often identical robots and the self-organised behaviour of the swarm as a whole. Robot swarms can be applied to a range of real world problem for example swarms of satellites to examine asteroids, robots swarms for search and rescue, for inspection of infrastructure or exploration and monitoring in extreme environments.

    It is challenging to design the behaviour of individual robots to ensure that required properties hold for the emergent swarm behaviour. Verification is the process that checks the system does satisfy these required properties. This is often carried out using simulation or real robot experiments. Formal verification is a mathematical analysis of systems using techniques such as model checking or temporal theorem proving.

    This project involves developing and applying formal verification to robot swarms. This can be used to ensure that the swarm achieves the required properties and to investigate the effects of failure of individuals. Wireless sensor networks have similar properties and this project could similarly be applied to them.

  • PhD Project 5: SALE-Auto: Safety Assurance for Learning-Enabled Autonomous Systems

    UoM Supervisory Team: Dr Youcheng Sun, Dr Louise Dennis, Prof Michael Fisher (Computer Science)

    Industry Supervisor: John Brotherhood (Jacobs)

    The SALE-Auto project aims at addressing the critical challenge of ensuring verifiable safety in cyber-physical systems, especially when including deep learning components. As autonomous systems continue to advance, their reliance on learning algorithms introduces complexities in guaranteeing safety.

    The project focuses on developing innovative methodologies and frameworks for the safety assurance of autonomous systems with deep learning capabilities. This would enable a corroborative verification approach in which safety features of an autonomous system could be analysed at differing levels of abstraction and these analyses compared to generate a better understanding of the safety of the system. SALE-Auto will integrate techniques from artificial intelligence, cybersecurity, and formal verification to for assuring the safety and reliability of learning-enabled autonomous systems.

  • PhD Project 6: TBIRD2 – Aerial Deployed Surface Vehicles

    UoM Supervisory Team: Dr Kieran Wood (Aerospace Systems), Dr Simon Watson (Electrical and Electronics Engineering)

    Industry Supervisor: Matthew Goundry (Jacobs)

  • PhD Project 7: State estimation for fault diagnosis and fault-tolerant control of redundant robotic systems

    UoM Supervisory Team: Dr Long Zhang, Dr Murilo Marinho, Dr Keir Groves (Electrical and Electronic Engineering),

    Industry Supervisor: Martí Morta Garriga (Jacobs)

    Robots working in the field usually face challenges unlikely to happen in a controlled environment such as research labs. Therefore, in real-world situations, robots are subject to failures such as faulty sensing and actuation and need to operate under non-ideal conditions to finish the mission until getting back to a repair location. For example, when working in harsh or industrial environments, some arm joints of a mobile manipulator might stop working due to dust ingress or overheating from excessive load. In that case, the robot could leverage the kinematic redundancy to cope with the faulty actuators without compromising the mission execution or, ideally, performance. Analogously, an underwater vehicle with many thrusters should operate appropriately if even some actuators stop working or work with reduced performance.

    This work will focus on general state estimation strategies for detecting faults in redundant systems and policies to choose further action, such as mobile manipulators composed of an articulated robotic manipulator serially attached to a mobile base or underwater robots equipped with many thrusters. The developed techniques will be applied to real robots owned by one of the project partners and relevant to the overall research programme.
  • General PhD Application: Express interest for future research topics

    CRADLE aims to recruit a cohort of 12 doctoral research students (PhDs) during its initial 5-year programme. Research topics are influenced by the industry needs that Jacobs caters to. 

    If you would like to get updates on future PhD studentship calls, send your CV and Cover Letter to us (see How to Apply).

Each project will be supervised by academics at the University of Manchester and experts in the application of robotic systems at Jacobs, and they will all contain a significant focus on the design and development of practical robotic demonstrators.

Entry Requirements

Applicants should have or expect to achieve at least a 2.1 honours degree in engineering or computer science or an MSc qualification in Robotics, Electrical & Electronic Engineering, Mechatronic Engineering, Aerospace Engineering, Mechanical Engineering, Computer Science, or other equivalent discipline.

Equality, diversity, and inclusion are fundamental to the success of The University of Manchester and are 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).

How to Apply

To express your interest in applying for a specific PhD project, email with the following details:

  • Subject: [PhD Applicant] Department – Research Topic
  • Attach your CV and cover letter.

Upon passing the initial screening, you will then be advised to submit an online application through the university website ( and 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
  • You will be asked to supply contact details for two referees on the application.
  • English Language certificate (if applicable)

Funding Notes

Funded by UoM and Jacobs as their support to the EPSRC-funded Prosperity Partnership (CRADLE: Centre for Robotic Autonomy in Demanding and Long-lasting Environments). 

  • Skills and academic background matching with the research project, and academic records (including transcripts) at the master’s level or equivalent degree level.
  • Creative and innovation potential, ability to think out of the box.
  • Motivation of the candidate for inter-disciplinary research.
  • Ability to work with both academics and industry partners.
  • Fully funded for UK home students, but only partial funding is available for overseas students.