Senior Research Associate in Machine Learning for Spontaneous Inner Speech Detection - 0927-25

Join the Senior Research Associate in Machine Learning for Spontaneous Inner Speech Detection role at Lancaster University.

Field: Psychology

Location: Bailrigg, Lancaster, UK

Salary: £39,906 to £48,882 – Part time, indefinite with end date

Closing Date: Sunday 04 January 2026

Interview Date: Monday 19 January 2026

Reference: 0927-25

Inner speech – talking to yourself in your mind – appears fundamental to human consciousness, thinking, and self‑reflection. Yet we have no reliable way to objectively detect or measure it as it happens spontaneously in everyday life. This project tackles one of cognitive neuroscience’s most challenging problems: detecting fleeting, spontaneous inner speech from the “haystack” of ongoing brain activity.

Can we objectively detect inner speech – the voice in your head – from brain signals? This project tackles one of cognitive neuroscience's hardest problems: identifying spontaneous inner speech from noisy EEG data without precise temporal labels. Traditional classification has failed because spontaneous inner speech is sparse and co‑occurs with other brain activities. We need novel ML approaches suited to weakly‑supervised settings, transfer learning, or contrastive methods to detect these fleeting cognitive events.

Working with Dr Bo Yao (Lancaster) and Professor Xin Yao (Lingnan University, Hong Kong), you will develop and validate a novel ML approach for inner speech detection from high‑density EEG data. The work is fast‑paced, requiring rapid prototyping with access to Lancaster's high‑performance computing facilities.

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• Working implementation of one novel detection approach
  
• Systematic validation against baseline methods
  
• Lead manuscript for publication
  
• Documentation of what works, what doesn’t, and why
  

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• PhD in Machine Learning, Computer Science, Computational Neuroscience, or related field
  
• Demonstrable experience developing deep learning models for time‑series/sequential data (EEG, biosignals, audio, sensor data, or similar)
  
• Strong Python skills with PyTorch or TensorFlow
  
• Proven ability to work independently on complex problems
  
• Excellent communication skills for interdisciplinary collaboration
  
• Right to work in UK for project duration
  

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• Experience with ML for unlabelled/sparsely‑labelled sequential data (self‑supervised learning, anomaly detection, domain adaptation)
  
• Model interpretability techniques (attention mechanisms, saliency mapping)
  
• Prior work with neuroimaging data or biosignals
  
• First‑author publications in ML or computational neuroscience
  

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• Intellectual freedom – real ownership of methods
  
• Cross‑disciplinary experience – apply ML expertise to neuroscience
  
• Fast‑track impact – see your methods in use within months
  
• Flexibility – 0.8 FTE for work‑life balance
  
• Strong mentorship – collaboration with experts in neuroscience and AI
  
• Career development – potential first‑author publication(s) at the intersection of AI and consciousness science
  

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• 25 days annual leave (pro‑rata) plus closure days and bank holidays
  
• Pension scheme and flexible benefits
  
• Athena Swan Silver Award department
  
• Flexible working arrangements
  

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• CV (standard academic format)
  
• Cover letter (max 2 pages) addressing:
  
  
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  • Your most relevant ML project with time‑series/noisy/weakly‑supervised data (your role, methods, outcomes)
    
  • One potential approach for detecting sparse, unlabelled events in noisy multivariate time‑series
    
  • Why this project appeals to you now
    
  
  
• Optional code sample: GitHub repo/notebook demonstrating your implementation style
  

For enquiries, contact Dr Bo Yao (). Apply online via Lancaster University Jobs Portal.

Mid‑Senior level

Part‑time

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Higher Education

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