Reference:
0181-26
Senior Research Associate in Machine Learning for Speech Processing
Department: Phonetics Laboratory / Linguistics and English Language
Location: Bailrigg, Lancaster, UK
Salary: £39,906 (pro-rata if part-time)
Contract: Full-time (1.0 FTE), fixed-term [18 months]
The Project
Modern machine learning can predict vocal tract shapes from audio recordings of the voice with remarkable accuracy, but most of these models are black boxes. This Royal Society-funded project aims to crack open the black box and solve one of the most compelling challenges in speech science: understanding the mapping between vocal tract movements and the acoustic speech signal. Using state-of-the-art MRI recordings of the vocal tract during speech, we aim to develop machine learning approaches that don’t just predict acoustic output from articulatory configurations, but reveal why and how these mappings work. We need approaches that combine predictive power with scientific insight: models whose internal representations align with phonetic and physical knowledge. This requires hybrid machine learning (ML) approaches that integrate domain knowledge with data-driven learning, as well as explainable AI (xAI) techniques that make model behaviour transparent and scientifically meaningful. You will apply these approaches to a large database of real-time MRI and acoustic recordings of the vocal tract. Solving this problem will help to drive fundamental progress on critical applications, such as articulatory biofeedback for language learning and speech therapy.
Your Role
Working with Dr Sam Kirkham (Lancaster, Speech Science), Dr Anton Ragni (Sheffield, Computer Science) and Professor Aneta Stefanovska (Lancaster, Physics) you'll develop and validate interpretable ML approaches for modelling acoustic-articulatory relations using MRI vocal tract data. The position is available for 18 months from 1 July 2026 (start date negotiable).
Key objectives
1. Develop hybrid ML architectures that incorporate phonetic and physical constraints.
2. Apply and extend explainable AI techniques for speech production modelling.
3. Validate model interpretations against established knowledge.
4. Lead and/or contribute to publications at the intersection of speech science and machine learning.
This is a methodologically creative role with genuine intellectual ownership. You'll have access to rich MRI datasets and Lancaster's high-performance computing facilities.
Essential Requirements
5. PhD in Speech Processing, Computational Linguistics, Machine Learning, Computer Science, or related field (PhD must have been submitted by start date).
6. Strong experience with machine learning for time-series data.
7. Excellent Python skills with PyTorch.
8. Ability to work independently on complex, open-ended problems.
9. Effective communication skills for interdisciplinary collaboration.
10. Demonstrated interest in interpretable ML, explainable AI, or hybrid approaches.
Desirable
11. Experience with speech/audio processing or articulatory data.
12. Knowledge of physics-informed neural networks, neural ODEs, or other hybrid architectures.
13. Knowledge of speech production, speech science, or biomechanics.
14. Publications in ML, speech technology, or computational linguistics.
Why This Role?
15. Intellectual ownership – shape the methodology, not just implement it.
16. Interdisciplinary impact – apply ML expertise to fundamental questions about human speech.
17. Publication opportunities – first-author papers bridging AI and speech science.
18. Flexibility – flexible working arrangements.
19. Strong mentorship – work with researchers spanning speech production, computational modelling, machine learning, and biophysics.
20. Career development – build expertise at the growing intersection of interpretable AI and speech processing.
Benefits
Lancaster University is highly ranked and research-led and situated near the historic city of Lancaster. The North West of England offers high standards of living, beautiful countryside, including the Lake District, and excellent national and international transport connectivity. See .
To Apply
Submit via the Lancaster University Jobs Portal:
21. CV (standard academic format)
22. Cover letter(max 2 pages) addressing: Your most relevant ML project involving interpretability, hybrid approaches, or sequential data.Your perspective on making ML models scientifically interpretable (not just explainable to end users).Why this project interests you.
Optional: Link to GitHub repository or code sample demonstrating your implementation approach.