Wenwu Wang

Applications are invited for two Research Fellow positions at the Centre for Vision Speech and Signal Processing (CVSSP) and the Surrey Institute of People Centred AI , University of Surrey , UK. The projects are focused on spatial music , machine learning , AI , signal processing , audio perception , and data curation . Position 1: Research Fellow on Machine Learning for Perceptual Quality Prediction of Spatial Music (12 months). This role develops machine learning and AI models and signal processing algorithms for perceptual quality prediction and rating of songs reproduced using 3D rendering algorithms. Position 2: Research Fellow on Perceptual Evaluations and Data Curation for Spatial Music (8 months). This role involves organising and performing listening tests, recording listening scores, performing statistical analysis of listening test data, and organising data files. The posts are based in CVSSP and will work under the direction of Prof Wenwu Wang in collaboration with an industrial partner. CVSSP is described as an international centre of excellence for research in audio-visual machine perception and AI, with extensive audio/video capture and analysis facilities and large GPU compute resources. Eligibility highlights: applicants should hold a PhD degree (or equivalent). For the machine learning role, relevant backgrounds include machine learning, spatial audio, audio signal processing, audio perception, audio quality assessment, electronic engineering, computer science, applied mathematics, or statistics. For the perceptual evaluation role, relevant backgrounds include electronic engineering, computer science, psychology of hearing, or related fields. Experience in machine learning/AI, spatial audio, listening tests, data curation, psychoacoustics, or music audio is preferred. Strong analytical and programming skills in Python, Matlab, or C/C++ are expected for the first role; dataset management and curation skills are expected for the second. Funding: both positions are funded by a leading industry partner. No salary amount is stated in the post. How to apply: apply online through the University of Surrey vacancy page with a CV and cover letter. Informal enquiries can be sent to Prof Wenwu Wang at [email protected] or via his personal academic page.

[Fully and directly funded for this project only. Home or International fees for 42 months. UKRI standard stipend £21,805 per annum (42 months) for academic year 2026/27. RTSG: £1,500 per year. Additional conference funding (up to £3,000) may be available, subject to approval.] This PhD studentship at the University of Surrey, in collaboration with Sony Interactive Entertainment, offers a unique opportunity to advance the field of automated bug discovery in video games using agentic AI. The project aims to develop a Quality of Experience (QoE)-driven multi-agent framework that automates bug discovery, reproducibility, and severity ranking in complex, interactive game environments. The research will focus on three agent roles: Explorer (actively probing games for failures), Inspector (verifying and reproducing issues with minimal evidence), and Reporter (generating structured bug reports for triage). A key innovation is the explicit QoE severity model, which ranks bugs based on predicted player impact, leveraging human feedback and QA expertise. Evaluation will be conducted against scripted and random baselines, using metrics such as unique bugs per hour, reproducibility rate, evidence quality, and correlation between severity ranking and human judgements. The student will benefit from industry-informed glitch taxonomies, reporting requirements, and co-supervision from SIE, ensuring relevance to real-world game development and QA practices. The project aligns with Surrey’s GAIN programme and SAHCI’s games provision, supporting standards-oriented impact through engagement with ITU-T gaming QoE initiatives. Funding is fully and directly provided for this project, covering home or international fees for 42 months, a UKRI standard stipend (£21,805 per annum), RTSG (£1,500 per year), and potential conference funding (up to £3,000). The studentship is open to UK and international candidates, starting in October 2026. Applicants should have strong programming skills (preferably Python), experience in machine learning/AI or software engineering for interactive systems, and ideally game development experience with platforms like Unity, Unreal, or Godot. Knowledge of generative AI, vision-language models, and agent-based AI systems is desirable, along with the ability to design and conduct human-centred evaluation studies. Applications should be submitted via the Innovative Media Technology PhD programme page. Instead of a research proposal, upload a document stating the project title and supervisor name. For enquiries, contact Dr Femi Adeyemi-Ejeye or Prof Wenwu Wang. The application deadline is 30 May 2026.

This collaborative PhD project between the University of Surrey (School of Arts, Humanities and Creative Industries) and Sony Interactive Entertainment (SIE) focuses on developing a Quality of Experience (QoE)-driven agentic AI framework for automated bug discovery in video games. Video games are increasingly complex and updated frequently, making manual quality assurance (QA) challenging and often insufficient for covering vast interactive state spaces. Automated approaches typically detect anomalies but lack clarity in issue explanation and prioritisation based on player impact. The research will implement a multi-agent workflow with three distinct roles: the Explorer agent actively probes games to discover failures; the Inspector agent verifies and reproduces candidate issues, capturing minimal but sufficient evidence (inputs, states, clips, logs); and the Reporter agent produces structured bug reports suitable for triage. A key innovation is the explicit QoE severity model, which learns to rank bugs based on predicted player impact—such as frustration, immersion disruption, fairness issues, comfort, or usability—using lightweight human feedback and QA expertise. Evaluation will focus on improvements over scripted and random baselines, using metrics like confirmed unique bugs per hour, reproducibility rate, evidence quality, and correlation between severity ranking and human judgements. The student will benefit from SIE co-supervision, industry-informed glitch taxonomies, and reporting requirements, all under appropriate data, IP, and publication governance. The project aligns with Surrey’s GAIN programme and games provision within SAHCI, supporting standards-oriented impact through the supervisory team’s engagement with ITU-T work on gaming QoE. Funding is fully and directly provided for this project, covering Home or International fees for 42 months. The UKRI standard stipend is £21,805 per annum for the academic year 2026/27, with an RTSG of £1,500 per year and potential additional conference funding up to £3,000, subject to approval. Applicants must meet the minimum entry requirements for the PhD programme at University of Surrey. Preferred backgrounds include Computer Science, Games Technology, Digital Media, or related disciplines. Strong programming skills (preferably Python) and experience in machine learning/AI or software engineering for interactive systems are required. Experience in game development or design using Unity, Unreal, or Godot is desirable. Up-to-date knowledge of generative AI, vision-language models, and agent-based AI systems is advantageous. Candidates should be able to design and conduct human-centred evaluation experiments, including QoE, usability, and preference studies, and demonstrate excellent communication skills and the ability to collaborate with industry partners under confidentiality constraints. The position is open to UK and international candidates, with a start date in October 2026. To apply, discuss your project with a prospective supervisor and submit your application via the Innovative Media Technology PhD programme page. In place of a research proposal, upload a document stating the project title and the name of the relevant supervisor.