Research Assistant in EEG Data Curation and Machine Learning

About us:

King's College London is a research-led university based in the heart of London, and the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) is one of Europe's largest centres for research and teaching in psychiatry, psychology and neuroscience.

The Department of Basic & Clinical Neuroscience brings together laboratory scientists, computational researchers and clinical academics working to understand the mechanisms of neurological disease and to translate that understanding into better treatment. This post sits within the department's epilepsy and computational neurophysiology research, and works closely with clinical colleagues at King's College Hospital, with the King's Institute for Artificial Intelligence, and with collaborators at UCL.

About the role:

We are looking for a Research Assistant to build and curate the EEG data resource underpinning a jointly funded research programme in machine learning for clinical neurophysiology. The post is funded by a King's AI+ Academic Fellowship and by a pilot grant from the Epilepsy Research Institute, and is based in the Department of Basic & Clinical Neuroscience.

Around a third of people with epilepsy continue to have seizures despite treatment, and the unpredictability of those seizures is consistently identified by patients as one of the hardest aspects of living with the condition. Progress in seizure forecasting has been held back by a reliance on painstakingly hand-labelled EEG, which limits how much data any model can learn from. This programme takes a different approach, using self-supervised learning to train models on very large volumes of unlabelled EEG before adapting them to clinical tasks.

None of that is possible without a well-built data resource, and that is what this post is principally for. You will collate EEG from several very different sources - ultra-long-term subcutaneous recordings, clinical scalp EEG from King's College Hospital, and public research datasets - into the project's secure shared computing environment, and curate them into a single documented, quality-controlled and governance-compliant resource. A substantial part of the role involves working directly with clinical colleagues, including the project's clinical physiologist, to obtain recordings and to understand how they were made and annotated.

You will then develop the pipelines that turn those data into something modellable: standardised preprocessing, channel harmonisation, quality control, and feature engineering that supports a range of downstream analyses. You will also contribute to the design and implementation of the self-supervised learning tasks used to pretrain models on the resulting data, and support training runs on high-performance computing resources. Throughout, you will be expected to write clear, reproducible, well-documented code, and to contribute to the reports, presentations and publications that come out of the work.

You will be line managed by Dr Richard Rosch and Dr Dominic Burrows and will work day to day with a wider team of clinical and computational collaborators. The role would suit someone with a strong quantitative and programming background who wants to develop research experience at the interface of research data engineering, machine learning and clinical neuroscience, and it provides a good foundation for anyone considering subsequent doctoral study.

This is a full-time post (35 hours per week), and you will be offered a fixed term contract of 12 months.

Research staff at King's are entitled to at least 10 days per year (pro-rata) for professional development. This entitlement, from the Concordat to Support the Career Development of Researchers , applies to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information.

About you:

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

1. A Master's degree (or equivalent qualification or experience) in bioinformatics, computational neuroscience, machine learning, data science, biomedical engineering or a closely related discipline.
2. Strong practical programming ability in Python for scientific computing, including use of version control (Git).
3. Experience of building and maintaining data processing pipelines for large or complex datasets, producing well-documented, reproducible and analysis-ready outputs.
4. Experience of working with biomedical or physiological time-series data (for example EEG, ECG or comparable signals), including preprocessing and feature extraction.
5. Practical experience of machine learning.
6. Demonstrable understanding of the responsibilities involved in handling sensitive clinical or personal data, and of good research data management practice.
7. Effective written and verbal communication skills, and the ability to work collaboratively with both clinical and computational colleagues.
8. Experience of working with EEG data specifically, including clinical, ambulatory or long-term subcutaneous EEG recordings.

Desirable criteria

1. Familiarity with BIDS or comparable community data standards for neuroscience data.
2. Familiarity with supervised learning approaches
3. Demonstrable interest in epilepsy, seizure prediction or translational clinical neuroscience, evidenced through prior study or project work.

Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process.

Further Information

At King's, we believe that the diversity of our community and a culture that is welcoming, open, inclusive and collaborative, are great strengths of the university.

The Equality Act of 2010 protects the rights of our students and staff and provides a framework to fulfil our duties to eliminate unlawful discrimination, harassment and victimisation and in addition, to advance equality of opportunity and foster good relations between those who share a protected characteristic and those who do not. At times, this will include balancing rights and beliefs that can feel in tension.

We are committed to free speech and to academic freedom, believing that our foundational purpose as a university, is to create spaces where a wide range of ideas, including ideas that are controversial, can be discussed and debated, and where members of our community can express lawful views without fear of intimidation, harassment or discrimination.

When engaging in the robust exchange of ideas, we ask that our community is mindful of our Dignity at King's guidance.

We welcome your thoughts on how organisations can create an inclusive environment, including one which supports free speech and the exchange of a wide range of ideas.

This post is subject to Disclosure and Barring Service clearances.