Let’s CollabAR-8: Improved Co-Dependent Interaction in Collaborative Virtual Environments
Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form.
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
Frequently Asked Questions
To help support you to complete your application, please consult the frequently asked questions below:
Project title: Let’s CollabAR-8: Improved Co-Dependent Interaction in Collaborative Virtual Environments
Project Lead: Professor Ian Williams, Professor of Visual Computing
Project ID: 02 - 45488280
Project description:
CollabAR-8 (kəˈlæb.ə.reɪt) explores the design, implementation, and evaluation of co-dependent, symmetric interaction within co-located Collaborative Virtual Environments (CVEs).
The focus of the CollabAR-8 PhD project is to enhance shared working environments in immersive systems, where users synchronously and interdependently manipulate shared virtual objects (e.g., selecting, translating, interacting with, and manipulating the same virtual scene objects). These types of experiences are becoming increasingly common in training, education, simulation, and entertainment domains where effective teamwork is essential.
While significant research has been dedicated to improving immersive experiences for individual users, far less attention has been given to collaborative virtual experiences, particularly those involving co-located users who share both physical and virtual spaces. As a result, the full potential of immersive technologies to transform the way we work, learn, play, and explore is often limited to single-user scenarios or poorly designed collaborative systems. The current development of CVEs lacks in-depth research into the complexities of shared collaborative work. Advancing CVEs especially in co-located, shared use contexts will not only expand the potential of immersive experiences but also support the development of innovative platforms and opportunities for team development.
Anticipated findings and contributions to knowledge:
CollabAR-8 will investigate this domain through three key research strands:
1. Embodied Interaction
This strand will explore embodied interaction in co-dependent virtual tasks, especially in co-located environments where both physical and virtual cues coexist and building on foundational work in embodied interaction.
2. Interaction Feedback
CollabAR-8 will study interaction feedback in the context of co-dependent manipulation within Level 3.2 cooperation. The framework of the human action cycle will guide the design of feedback mechanisms across co-planning, co-intent, co-execution, and co-evaluation stages.
3. Mixed Ability Asymmetrical Interaction Approaches
Interaction techniques greatly influence coordination and task performance in CVEs. However, many are designed with a "one-size-fits-all" approach, failing to support inclusive, mixed-ability teamwork. This strand will explore combinations of interaction techniques such as Grasp, AirTap, EyeGaze, HeadGaze, and various levels of interaction asymmetry (e.g., differing motor or gesture capabilities).
Person Specification:
Qualifications:
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Applicants should hold or expect to attain a good Master's degree or equivalent qualification in a relevant discipline such as Human-Computer Interaction, Virtual/Augmented Reality, Interaction Design, User Experience, Computer Science, Psychology, Cognitive Science, or related fields.
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Exceptional candidates with a strong Bachelor's degree and substantial research or industry experience may also be considered.
Experience and Knowledge:
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Proven knowledge or practical experience with immersive systems, virtual reality (VR), augmented reality (AR), or mixed reality (MR).
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Familiarity with collaborative virtual environments (CVEs) or multi-user interactive systems.
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Experience or foundational understanding in human-computer interaction (HCI), embodied interaction, user experience (UX) design, computer science, or cognitive psychology.
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Familiarity with frameworks such as the human action cycle and interaction feedback mechanisms is advantageous.
echnical Skills:
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Demonstratable proficiency in developing interactive experiences or simulations using popular immersive technology frameworks, games engines and software development environments (e.g., Unity, ARCore, ARKit).
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Demonstratable competence in programming languages such as C#, JavaScript, Python, C++.
Research and Analytical Skills:
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Strong analytical skills to design, conduct, and evaluate empirical user studies.
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Ability to critically review literature, synthesize findings, and communicate insights effectively.
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Strong scientific writing and reporting skills
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Experience with both qualitative and quantitative research methods, including user testing, interviews, technical data acquisition and processing
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Experience in statistical analysis and a strong level of confidence in numeric analysis and presentation.
Personal Attributes:
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Highly motivated with a clear passion for immersive technologies, collaborative systems, and improving user experiences through innovative design.
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Ability to lead and manage projects and design measurable project goals.
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Excellent interpersonal and collaborative skills, demonstrating the ability to work effectively both alone and within interdisciplinary teams.
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Ability to independently manage research tasks, demonstrating self-direction and initiative, balanced with openness to feedback and collaboration.
Professional Development:
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Openness to continuous professional development, actively seeking to expand knowledge and technical skills throughout the PhD programme.
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Participation in academic communities through conferences, workshops, and collaboration with external academic and industry partners.
Additional Desirable Attributes:
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Previous experience with projects involving XR and co-located user collaboration or multi-user interactions.
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Published or presented work in relevant fields, demonstrating capability in academic dissemination.
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.
Contact:
If you have any questions or need further information, please use the contact details below:
- For enquiries about the funding or project proposal, please contact ian.williams@bcu.ac.uk
- For enquiries about the application process, please contact: research.admissions@bcu.ac.uk