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Permanent Assistant and Associate Professor Postitions

We are looking to recruit Assistant/Associate Professors in Robotics and AI to the School of Computer Science to complement our strengths and join our Cyber-physical Health and Assistive Robotics (CHART) research group.

Cyber-physical systems and assistive robotics are becoming recognised as transformational technologies in helping people improve their quality of life and live independently. The CHART research group is spearheading research in this area with projects such as the EPSRC Health Technology Network+ EMERGENCE: Tackling Frailty – Facilitating the Emergence of Healthcare Robotics from Labs into Service, an EU Project METRICS, “Metrological Evaluation and Testing of Robots in International CompetitionS developing methods and benchmarks for assistive robots, working with AgeUK in trialling telepresence robots in residential villages, together with several Trustworthy Autonomous Systems projects.

As part of the Cobot Maker Space on Jubilee campus, the group is linked to the Smart Products Beacon and the Horizon CDT. Our focus is on addressing the highest priority healthcare challenges and have links with the Centre for Healthcare Technologies, the Queen’s Medical Centre and the National Rehabilitation Centre.

We are looking to develop our research capabilities in areas such as intelligent socially and physically assistive robots to support activities of daily living, mobility and rehabilitation, teleoperation and shared-control for remote assistance and treatment, intelligent sensing and mixed-reality environments for physical and cognitive rehabilitation, and cognitive architectures for embodied intelligence.

Assistant Professor: you will have a growing national reputation and the potential to make a significant impact on research and teaching.

Associate Professor: you will have a substantial and growing national and international reputation and will make a significant impact on research and teaching. You would act as principal investigator on major research projects, interpret and review research project outcomes and contribute to curriculum leadership.

For an informal chat please email

For further information and to apply please visit

The closing date for this recruitment round is 1st of March 2023

Funded PhD Research Opportunities

    • The Horizon CDT is looking for 15 talented and enthusiastic students to join the Horizon Centre for Doctoral Training (CDT) in September 2023. The Horizon CDT is an interdisciplinary Centre welcoming applicants from a wide range of backgrounds including computer science, engineering, mathematics, human factors, human-robot interaction, psychology, sociology, business, geography, social science, medicine/health sciences and the arts. Applicants must demonstrate an enthusiasm for transdisciplinary research, with a 2:1 honours degree, or a combination of qualifications and/or experience equivalent to that level.

    • You will receive a generous enhanced, tax-free stipend of £19,891 per annum (this is the current 2022/23 rate – awaiting confirmation of rate for 2023/24).

    • For more information and to apply, please visit

    • The closing date for this recruitment round has now passed. Please keep a lookout for announcements for upcoming rounds.

The CHART Research Team are offering several projects in collaboration with their partners:

Multimodal interfaces to enable multisensory accessible interaction in remote cultural environments through telepresence robots

To discuss this project please email:

Partner: Screen South

Telepresence robots offer a significant digital opportunity for people to remotely access social, work and cultural spaces, autonomously moving around them, giving a feeling of connection and presence. As such, telepresence robots can be a transformative tool in enabling engagement with museums and galleries, making connections and improving wellbeing. For a number of disabled people, and those shielding due to lowered immunity due to long-term conditions, having the choice to access cultural spaces and interact with people and objects through telepresence robots, can offer more freedom and flexibility to be ‘present’ in locations.

However, the interfaces to control telepresence robots can be cumbersome and inaccessible, particularly for those with sensory and/or physical impairments, making it difficult or impossible for them to use these effectively. We are also interested in exploring how by combining telepresence robots with other digital devises, such as VR and haptics, we can enable truly immersive multisensory experiences that are accessible to a variety of participants.

The aim of this research is to co-design and test a range of different input and output devices and modalities to develop multisensory interfaces that will enable accessible, smooth and enjoyable control and remote interaction. You will explore the integration and use of speech, head and ear-switches, electromyograms, and gaze, amongst other modalities, for control, and visual, haptic and aural modalities for feedback of information to enable rich and creative experiences of the remote space, people and objects. You will study and develop metrics for evaluating usability and user experience for accessible teleoperation using these modalities and custom devices, as well as developing a best practice framework to support future accessible design. The research will also offer the opportunity to draw on disability studies research to understand the lived experience of using telepresence in different contexts, understanding impact on self-efficacy, identity, social relationships and agency in interactions. This research offers several technical and non-technical strands to explore, based on the candidate’s background, skills and experience.

Ambient and Augmented Reality Information Visualisation of Smart Sensor Data for Real-Time Clinical Decision Making

To discuss this project please email:

Partner: Queen’s Medical Centre, University Hospital, Nottingham

In busy clinical environments, particularly where patients have a high-level of staff dependency, providing support for clinical staff to improve patient monitoring, triage and management can not only help to ease level of staff stress, but also potentially improve patient safety. This research will investigate how information to assist with clinical decision making can be presented through creative ambient and/or augmented information displays and the impact that different modes and modalities have on user cognitive load, attention and efficiency. This research is situated in the use of tangible devices, and ambient and augmented reality displays, exploring topics in information visualisation, sensory substitution, human factors and user experience design. Considering the context of high-pressured environments, such as dementia wards, you will begin the research with a qualitative observational study, scoping the requirements using co-design with clinical and care professionals, before designing, developing and evaluating a range of approaches for representing the required information.

Based on the candidate’s academic background, skills and experience, the research focus can be either on developing intelligent sensing to capture and represent the key information required for decision-making, or design and development of the approaches for displaying it through different means and modalities, or a combination of both.

Intelligent sensing and machine learning to adapt social robot assistance to support independent living

To discuss this project please email:

Partner: Robotics For Good CIC

Assistive technologies, such as smart home environments, integrated sensors and service robotics are recognised as emerging tools in helping people with long-term conditions improve their quality of life and live independently for longer. A key aspect of the research into assistive robotics for assisted living is developing contextual and social intelligence for the robot to interact appropriately, safely, and reliably in real-time. This research relates to developing assistive robot behaviour by incorporating both environmental and user data, and behaviour, as part of an overall intelligent control system architecture.

In addition to having a ‘memory’ of previous interactions and situations, assistive robots need access to information that is current and one that provides a dynamic world view of the user (including their emotional state) so that they can provide information and responses that are contextually appropriate. Typical activities for which support can be provided is support with rehabilitation, medication management, cognitive and social stimulation, nutrition management etc. Drawing on information from environmental and activity sensors instrumented into a smart home, and information about the user’s current physical and emotional state, assistive robots can potentially create value through provision of interventions that are more socially intelligent regarding how, and what advice and support they provide. To create a more holistic service, that takes into consideration prioritisation of events based on aspects of health and social circumstance requires an adaptable, intelligent learning system. Building on existing research on intelligent control system architectures, the aim of this research will be to design and test modular semantic memory architectures that can be adapted over time. You will investigate optimal combinations of contextual data comprising implicit (emotional, physiological) and explicit user data (interaction), as well as behavioural activity data assimilated from a range of wearable and smart home sensors, to develop adaptive, intelligent and emotionally engaging robot behaviour to support independent living.

Learning, user modelling and assistive shared control to support wheelchair users

To discuss this project please email:

This PhD project will develop on the Nottingham Robotic Mobility Assistant, NoRMA ( to study triadic learning methodologies for developing effective assistance policies for wheelchair users to support their day to day activities.

Long term autonomy and mobile inspection of extreme environments with a quadruped robot

To discuss this project please email:

This PhD project will be in collaboration with RACE ( and aims to develop incremental learning methodologies to develop context-based policies, not only for navigation, but error recovery in long term automation. Human-in-the-loop and teleoperated control methods will be used as the backbone strategy to ensure increasing levels of autonomy during inspection. We will look at human-robot interaction methodologies for efficient management and optimisation of parallel tasks encountered in day-to-day operation of the Boston Dynamics Spot mobile inspection robot.

Exploring Bilateral Trustworthiness in Human-Robot Collaborative

To discuss this project please email:

This PhD studentship will investigate trust from a theory of mind point of view to model a robot’s trustworthiness from the perspective of a human, and vice versa.

Lifelong learning with robotic vacuum cleaners in social spaces: In collaboration with Beko Plc. (, this PhD project will focus on these challenges by targeting multiple strands of research in perception, planning, human-in-the-loop learning, and shared control for service robots. The ability to detect and recover from errors during navigation is an essential ability for an autonomous service robot that can run for extended periods of time. In addition, functioning in human settings, these robots should be programmed to adhere to social cues in a context- dependent manner, not only to enable safe, but also acceptable functionality.

Fully Funded Studentships in the School of Computer Science

We offer a range of fully-funded PhD Studentships in the School of Computer Science. Please get in touch if you are interested in any of the topics listed below or any others based on your interests that you want to discuss with the CHART team that correspond to their research focus.

Closing Date: Sunday 12 February 2023

Reference: SCI2122

Applications are invited from International and Home students for fully-funded PhD studentships offered by the School of Computer Science at the University of Nottingham, starting on 1st October 2023.

The studentships available are fully funded for 3.5 years and include a stipend of (minimum) £16,062 per year and tuition fees.

The topics for the studentships are open, but your research proposal should relate to the interests of one of the CHART research groups' Topics of Interest as listed below.

Entry Requirements:

Qualification Requirement: Degree 2:1 or masters in computer science or another relevant area

International and EU equivalents: We accept a wide range of qualifications from all over the world. For information on entry requirements from your country, see our country pages.

IELTS 6.5 (6.0 in each element)

English language requirements As well as IELTS (listed above), we also accept other English language qualifications. This includes TOEFL iBT, Pearson PTE, GCSE, IB and O level English.

Application process:

Please check your eligibility against the entry requirements prior to proceeding.

If you are interested in applying, please contact potential supervisors to discuss your research proposal.

If the supervisor wishes to support your application post interview, they will direct you to make an official application through the MyNottingham system. You will be required to state the name of your supervisor and the studentship reference number in your application.

Do not submit your application via the My Nottingham platform without having confirmed support of a supervisor first. Please email the person/people named next to the topic you are interested in with an up-to-date copy of your CV, marks transcripts, and a cover email explaining why you will be suitable for the selected PhD topic. We will then be able to advise you whether to proceed with a formal application on My Nottingham or not.


  • Safety

    • Analysis of the impact of cognitive loading and distractions during human-robot collaboration for assistive tasks (Praminda Caleb-Solly)

  • Embodied intelligence and sensing

    • Intelligent sensing and machine learning to improve the diagnosis and treatment of children with movement disorders (Alex Turner)

    • Design of smart actuated sensing devices and environments to support cognitive function/diagnostics in assisted living contexts (Praminda Caleb-Solly/Armaghan Moemeni)

    • Cyber-physical Space in Personalised Ambient Assisted Living (AAL) - Digital Twin/Blockchain/Machine Learning (Armaghan Moemeni)

    • Intelligent sensing to measure human trust using physiological sensing in virtual reality - for application of cognitive training and support (Armaghan Moemeni)

  • Accessible Interaction

  • Modular robotics

  • Telepresence and Teleoperation

    • Multimodal real-time feedback (haptic, auditory, visual) for teleoperation of assistive and rehabiliation tasks (Praminda Caleb-Solly)

  • Autonomous and tele-manipulation

    • Improving autonomous complex robot manipulation capabilities that go beyond just grasping (Ayse Kucukyilmaz)

  • Shared and traded control

    • Modulation of levels of autonomy in human-robot teamwork through shared and traded autonomy paradigms (Ayse Kucukyilmaz)

  • Assisted Mobility

    • Designing and developing learning-based methodologies for wheelchair driving assistance (Ayse Kucukyilmaz)

    • Enhancing driving performance and safety using AR and haptics technologies in robotic wheelchairs (Ayse Kucukyilmaz)

    • Multimodal feedback for shared control of Early Years Powered Mobility (children's wheelchairs) to support independent mobility (Praminda Caleb-Solly)

Where to find us

We are located in the Cobot Maker Space in the Nottingham Geospatial Institute On Jubilee Campus, University of Nottingham