We are working to create a world-class Data Science and AI @ Lancaster Institute (DSAIL) that sets the global standard for a truly interdisciplinary approach to contemporary data-driven research challenges. DSAIL aims to have an internationally recognised and distinctive strength in being able to provide an end-to-end interdisciplinary research capability - from infrastructure and fundamentals through to globally relevant problem domains and the social, legal and ethical issues raised by the use of Data Science.

The Institute is initially focusing on the fundamentals of Data Science and AI including security and privacy together with cross-cutting theme areas consisting of environment, resilience and sustainability; health and ageing, data and society and creating a world-leading institute with over 350 affiliated academics, researchers, and students.

Our data science, AI, health data science and business analytics programmes have launched the careers of hundreds of data professionals over the last 10 years. Students from our programmes have progressed to data science roles at Amazon, PWC, Ernst & Young, Hawaiian Airlines, eBay, Zurich Insurance, the Co-operative Group, N Brown, the NHS and many others - please look at our Education pages for further details of the courses on offer.

PhD project description: Inner speech is functionally involved in memory, planning, and self-regulation. It also manifests as verbal rumination, a feature of depression and anxiety, and is implicated in voice-hearing experiences in psychosis. Existing approaches mostly use silent-speech paradigms in which inner speech is produced on cue, but ruminative and intrusive thoughts arise spontaneously, so capturing them requires methods that work in naturalistic conditions.

This PhD will develop such methods, combining wearable EEG with portable ultrasound imaging of the tongue. The student will work at the intersection of cognitive/computational neuroscience, machine learning, and articulatory phonetics. They will translate an existing inner-speech classifier from research-grade laboratory EEG to a wearable form factor, and develop deep-learning models that identify spontaneous inner-speech episodes and characterise their emotional valence. The project will also establish what is achievable with wearable EEG alone, a critical step towards future clinical applications.

The student will be based in the Language, Inner Speech, and Neuroscience (LISN) Lab within the Department of Psychology, and embedded in the Imaging and Computer Vision Lancaster (iCVL) group (Computing) and the Phonetics Lab (Linguistics). They will be part of the DSAIL doctoral cohort, which brings together AI and data-science PhD students from across the university, with its own training programme, annual research events, and computational resources including Lancaster’s High-End Computing (HEC) GPU cluster. The Department of Psychology holds an Athena Swan Silver Award and supports an active research culture of seminars, writing workshops, and PhD-led activities.

Training and skills: Supervision is interdisciplinary, spanning cognitive neuroscience, machine learning, and articulatory phonetics. The student will develop hands-on skills in experimental design for EEG, ultrasound, and behavioural data collection; deep-learning model development; multimodal signal processing and fusion; articulatory speech analysis; and reproducible research practice with open-source software release. Outputs from the project will span cognitive neuroscience, machine learning, and phonetics venues.

Application details: The successful candidate will hold, or expect to obtain, at least an upper-second-class honours degree, and ideally a Masters, in computational neuroscience, computer science, or a closely related quantitative discipline. Strong Python programming skills and demonstrable machine-learning experience are essential. Applicants should complement this technical expertise with familiarity with neuroscientific or psychological experimental design - for example, in language, cognition, or inner speech research. Prior experience in signal processing, deep learning, and/or EEG and neuroimaging analysis is highly desirable.

Funding details: This 3.5-year full-time studentship is open to UK home students. It covers UK home fees, a tax-free stipend at the UKRI minimum rate for 2026/27 (£21,805 p.a.), paid subject to satisfactory progress, and a Research Training Support Grant (RTSG) of £1,000 per year for research-related expenses, conferences, and training. Additional cohort training and computational resources are provided through DSAIL.

How to apply (note there are 2 elements to the process): Please apply though the official University process. Instructions for applying through the University can be found at the Lancaster University Admission Portal. Please also submit your application materials via this Qualtrics form, which is specific to this PhD opportunity.

The materials required are:

• Academic CV along with the names and contact details of two referees who will be contacted directly (at least one must be academic).
• A sample of your work, either a code portfolio (e.g., a GitHub repository, a machine-learning project write-up, or other evidence of independent technical work) or an academic writing sample (e.g., an undergraduate or Masters dissertation, a coursework essay, or a peer-reviewed journal article). Both are welcome if you have them.
• A two-page personal statement / cover letter explaining your motivation and readiness for undertaking this PhD project.
• For the LU application, in place of a Research Proposal, you may upload a copy of this advertisement.

Dates

• Application deadline: 30 June 2026
• Provisional interview date: week beginning 20 July 2026
• Studentship start date: 1 October 2026

Informal enquiries: Prospective applicants are warmly encouraged to get in touch before applying. Please contact Dr Bo Yao (b.yao1@lancaster.ac.uk) for informal enquiries about the project, the supervisory team, or the application process.

PhD project description: Remote robotic systems are increasingly used in safety-critical environments such as nuclear decommissioning, infrastructure inspection, offshore energy and hazardous industrial operations. These systems depend on effective shared autonomy between human operators and robotic agents. However, real deployments often involve degraded communication, including latency, packet loss and bandwidth fluctuation, as well as changing operator states such as fatigue, overload or reduced readiness to intervene. Existing shared-autonomy approaches often treat these issues as external disturbances or rely on binary switching between teleoperation and autonomy, which limits robustness in realistic operating conditions.

This PhD will develop a new framework for communication-aware shared-autonomy allocation. The student will investigate how operator condition, network quality and task context can be represented in an uncertainty-calibrated allocation state, and how this state can be used to determine when the robot should follow human commands, restrict its action space, increase autonomy, or enter a conservative recovery-oriented mode. The project will combine AI, probabilistic modelling, robotic control and human-robot interaction, with validation through simulation, digital twins and embodied robotic experiments.

The student will be based in the School of Engineering and embedded in Lancaster’s Data Science and AI Institute. They will work across robotics, AI and communications, with access to Lancaster robotics facilities, including remote manipulation platforms, human-robot interaction interfaces, simulation environments and mobile robotic systems. The student will also be part of the DSAIL doctoral cohort, which brings together AI and data-science PhD students from across the university through cohort training, research events and interdisciplinary networking.

Training and skills: Supervision is interdisciplinary, spanning robotics, AI, wireless communications, human-robot interaction and safety-critical systems. The student will develop skills in probabilistic and uncertainty-aware machine learning, multimodal human-robot interaction data analysis, communication-aware autonomy, ROS/ROS2 robotic programming, digital-twin validation, constrained control and experimental robotics. Outputs from the project are expected to target leading robotics, AI and cyber-physical systems venues.

Application details: The successful candidate will hold, or expect to obtain, at least an upper-second-class honours degree, and ideally a Masters, in Engineering, Computer Science, Robotics, Artificial Intelligence, Control, Communications, Data Science or a closely related quantitative discipline. Strong programming skills in Python, MATLAB, C++ or similar are expected. Experience or strong interest in robotics, machine learning, probabilistic modelling, human-robot interaction, control systems, communication networks, ROS/ROS2, robotic simulation or safety-critical systems would be advantageous.

Funding details: This 3.5-year full-time studentship covers UK Home tuition fees, a tax-free stipend at the UKRI minimum rate for 2026/27 (£22,780 p.a.), paid subject to satisfactory progress, and a Research Training Support Grant (RTSG) of £1,000 per year for research-related expenses, conferences and training. Additional cohort training and interdisciplinary research opportunities are provided through DSAIL.

The studentship is primarily available to home applicants. Exceptional international applicants may be considered subject to eligibility, institutional approval, and confirmation of how any fee difference would be covered.

How to apply: Please apply through the official Lancaster University application process. Instructions for applying through the University can be found at the Lancaster University Admission Portal. Applicants should include:

* An academic CV, including the names and contact details of two referees, at least one of whom should be academic.

* A two-page personal statement / cover letter explaining your motivation, relevant experience and readiness for undertaking this PhD project.

* Academic transcripts.

* Evidence of technical work where available, such as a code portfolio, GitHub repository, robotics or machine-learning project, dissertation, coursework project or publication.

* For the Lancaster University application, in place of a Research Proposal, you may upload a copy of this advertisement.

Dates

• Application deadline: TBC
• Provisional interview date: TBC
• Studentship start date: 1 October 2026

Informal enquiries: Prospective applicants are warmly encouraged to get in touch before applying. Please contact Dr Ziwei Wang (z.wang82@lancaster.ac.uk) for informal enquiries about the project, the supervisory team or the application process.

AI Summit: Trust and Truth
Date: Tuesday 9th June
Time: 10:00 AM - 4:00 PM

Venue: LT3

9:30 - 10:00 (Break out space)
Coffee on arrival. Poll/survey.

10:00 - 10:30
Welcome and introductions from hosts Lee (Marketing) and Issy (Forensic Linguistics).

1030-1045

Share results of poll. Audience discussion.

1045- 1130

Talk 1: Dr Andrew Hardie (Linguistics)

How LLMs work and the uses of Generative AI

11:30-12:15

Talk 2: Dr Ziad Elsahn (Strategy)

How AI is used in teaching a management simulation

12:15-12:45

Panel 1: Department policies, teaching experiences

Panellists: Dr Simone Corsi (Strategy), Dr Andrew Hardie, Dr Ziad Elsahn

12:45-13:30 (Breakout space)

Lunch (provided to registered attendees)

13:30-14:15

Talk 3: Anna Galindo (Marketing)

How AI is impacting integrity and pedagogy

14:15 to 15:00

Talk 4: Dr Elisavet Christou (OWT)

How students want to use AI in assignments

15:00- 15:20

Panel 2: The future role of assessment, integrity and universities in the age of AI

Panellists: Dr Kirsty Dunn (Psychology), Dr Beatriz Rodriguez Garcia (Marketing), Dr Elisavet Christou, Dr Huw Fearnall-Williams (OWT)

15:20- 15:50

Talk 5: Brian Green (ISS)

SAIL- a secure place to use AI without needing personal subscriptions.

15:50 to 16:00

Closing takeaways, and next steps.

This year’s particular theme will be protection, privacy, and proactive synthesis.

In different words, we’re looking at questions like: is it possible to protect our voices from malicious actors who might want to clone us whilst still being recognisable to people around us? And further along the scale, can AI keep our identities fully withheld in sensitive contexts such as when we’re giving anonymous tip-offs, providing evidence in risky criminal proceedings, or even working undercover? And how much of our linguistic output do we need to think about when attempting this? We’ll start the summer school with interactive experiments and bring you back the results by the end. What will those experiments be? Well, you’ll have to attend to find out more.

Note that if you attended the in-person event in 2025, to keep it inclusive and accessible, we recap some of the same basic concepts, but then we move into new territory.

More information: https://wp.lancs.ac.uk/factor/summer-school/ss2026/

Book your place:https://lancasteruk.estore.flywire.com/products/factor-forensic-linguistics--speech-science-summer-school-2025-311382

Note that last year, tickets sold out a few weeks before the event. (We knew it was going to be big but even we were surprised at how fast they disappeared.) All that to say, if you’ve been waiting for some sort of cosmic Bat Signal, here it is.