Hi Everyone, I’m getting in touch as we're recruiting two PhD students for two fully-funded four year Centre-UB studentships<https://www.centre-ub.org/> at the University of Birmingham<https://www.birmingham.ac.uk/>. I’ve included the details below. If you know anyone who might be interested, I’d be very grateful if you could share this with them. Alternatively, would you be able to forward the information more broadly to students on your UG or Master’s courses? Many thanks in advance, and please let me know if you have any questions. Best wishes, Melissa Human-Aligned Super-Resolution for Facial Identification: Behavioural Evaluation, Bias Analysis, and Explainable AI – with VisionMetric Ltd Project supervised by Dr Melissa Colloff<https://www.birmingham.ac.uk/staff/profiles/psychology/colloff-melissa> <https://www.birmingham.ac.uk/staff/profiles/psychology/colloff-melissa> (Psychology), Professor Howard Bowman<https://www.birmingham.ac.uk/staff/profiles/psychology/bowman-howard> (Computer Science & Psychology) and Professor Heather Flowe<https://www.birmingham.ac.uk/staff/profiles/psychology/flowe-heather> (Psychology) together with Dr Stuart Gibson from VisionMetric Ltd<https://visionmetric.com/>m/>. Application deadline: Tuesday February 17th 2026, 5pm Projects now available to view on the Centre-UB website: https://www.centre-ub.org/studentships/new-opportunities/ Background CCTV footage is a predominant and crucial source of evidence in policing, with an estimated 21 million cameras operating in the UK. Yet more than 80% of real-world footage is too poor in quality to support reliable person identification. This severely limits investigative success and leaves offenders unidentified. Generative AI–based super-resolution (SR) technologies—such as VisionMetric’s iREVEAL—promise transformative gains by enhancing low-quality facial images. However, there is little scientific evidence on whether these tools improve human accuracy, how they affect machine recognition, and whether they introduce demographic biases. This interdisciplinary PhD (Psychology w. Computer Science) will investigate how generative AI–based super-resolution (SR) technologies influence human and machine-based facial identification. The PhD will combine behavioural experiments, machine learning, and explainable-AI methods to answer questions: 1. Do SR techniques improve human face identification accuracy? 2. How do SR-enhanced images affect machine-based facial recognition, and where do human and machine decisions diverge? 3. Do SR methods perform equitably across demographic groups? 4. Can SR models be improved using human perceptual insights? This project provides extensive interdisciplinary training from subject experts and industry, including in behavioural experimental design and statistical modelling; computer vision and AI techniques; explainable AI and human–machine comparison methods; and responsible innovation. The student will work closely with VisionMetric<https://visionmetric.com/>m/>, which is a leading SME supplying facial software to police forces in over 30 countries. Two placements at VisionMetric will provide hands-on experience with AI development pipelines and product development. This is an exceptional opportunity to build a skillset spanning psychology, AI, fairness, and forensic technology, positioning the candidate for careers in academia, applied behavioural science, AI research, technology, or policy. The project addresses both the societal risks and potential benefits of AI in high-stakes environments. Candidate We are looking for a highly talented and dedicated student with a 1st class or 2:1 undergraduate degree in Psychology, Cognitive Science, Computer Science, Neuroscience, Data Science, or a related field. An MSc degree in a relevant area is desirable though not necessary. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Applicants with an interest in human perception, AI ethics, or forensic science are especially encouraged. Interviews for this studentship are expected to take place on 16th March 2026. To apply for this studentship, please submit your application using this link<https://app.geckoform.com/public/#/modern/21FO00gwt7o1i40093m7p30d6…6e>: https://app.geckoform.com/public/#/modern/21FO00gwt7o1i40093m7p30d6e Further details on the application process can be found here, should applicants request them: https://www.centre-ub.org/studentships/application-process/ Informal enquiries about the project prior to application can be directed to Dr Melissa Colloff (m.colloff(a)bham.ac.uk). ———————————————— Enhanced Eyewitness ID: Predicting and Optimising ID Accuracy Through Behavioural Analysis – with Promat Project supervised by Prof Heather Flowe (Psychology), Dr Jizheng Wan (Computer Science), and Dr Melissa Colloff (Psychology) together with Mr Matt Whitwam from Promat.<http://promaps.software/Identification-Parade.aspx> Application deadline: Tuesday February 17th 2026, 5pm Projects now available to view on the Centre-UB website: https://www.centre-ub.org/studentships/new-opportunities/ Background Accurate eyewitness identification is critical for criminal investigations and public safety. Yet, despite major advances in psychological science, police lineup procedures have changed little in over a century. Most forces still rely on static photographs, and methods that struggle to capture the conditions under which crimes actually occur, such as poor lighting, variable viewpoints, and the use of disguises. This interdisciplinary PhD offers an exciting opportunity to help modernise eyewitness identification by combining cognitive psychology, immersive technology, and artificial intelligence. The project will test participant witnesses using a mock witness paradigm. Witnesses will be able to adjust lighting, toggle disguise features, and control viewing angle during lineups, creating a memory-congruent identification environment. The project will examine whether these reinstatement opportunities improve accuracy relative to standard, non-adaptive lineups, and how witnesses naturally explore faces under these conditions. A core innovation of the project is the integration of behavioural data with AI. The student will analyse eye movements, exploration patterns, and verbal reports to develop computational models that predict identification reliability. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and reinforcement learning approaches that adapt lineup presentation in real time based on witness behaviour. A defining feature of the project is close collaboration with Promat, the leading provider of police lineup software in the UK. Through this partnership, the student will gain first-hand experience working with real operational systems, understanding industry constraints, and contributing to research with direct pathways to deployment in policing practice. Joint supervision from Psychology and Computer Science will ensure strong interdisciplinary support while bridging academic research and industry innovation. Candidate We are looking for a highly talented and dedicated PhD student with a 1st class or 2:1 degree in the field of Psychology, Cognitive Science, Computer Science, Neuroscience, Data Science, or an allied field. An MSc degree in a relevant area is desirable though not necessary. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship are expected to take place on 20th March 2026. To apply for this studentship, please submit your application using this link<https://app.geckoform.com/public/#/modern/21FO00gwt7o1i40093m7p30d6…6e>: https://app.geckoform.com/public/#/modern/21FO00gwt7o1i40093m7p30d6e Informal enquiries about the project prior to application can be directed to Professor Heather Flowe (h.flowe(a)bham.ac.uk). Dr Melissa Colloff (she/her) Associate Professor of Forensic Psychology University of Birmingham School of Psychology B15 2TT Edgbaston, Birmingham, B15 2TT, UK office: 324, 52 Pritchatts Road. phone: +44 (0)121 4144925 email: m.colloff@bham.ac.uk<mailto:m.colloff@bham.ac.uk> www.birmingham.ac.uk<http://www.birmingham.ac.uk> Lab Website<https://www.appliedmemorylab.co.uk/> / Lab LinkedIn<https://www.linkedin.com/company/applied-memory-labs/> Personal Website<https://www.melissacolloff.com/> / Personal LinkedIn<https://www.linkedin.com/in/melissa-colloff-a5363a58/> I work full-time, but my non-working day is Wednesday. I work flexibly and sometimes send emails at odd times. Please do not feel obliged to reply to this email outside of your normal working hours. [University of Birmingham logo]