Senior Research Officer (Internal) - Csee in Colchester

*** Internal Applicants Only ***

Department

The School of Computer Science and Electronic Engineering has a strong, internationally recognised research profile spanning artificial intelligence, machine learning and biomedical signal processing. The School hosts active research in neurotechnology and brain-computer interfacing, and provides access to substantial computing resources, including a GPU-enabled high-performance computing cluster. This post sits within PaiNeuro, an interdisciplinary project working towards objective pain measurement using neurophysiological signals, in collaboration with Vestre Viken Hospital Trust in Norway.

Duties of the Role

Working under the direction of the Principal Investigator, Dr Sebastian Halder, the post holder will carry out the advanced computational analysis for the project, developing and evaluating machine-learning methods that distinguish pain-related from pain-free states in clinical EEG, with particular emphasis on models that generalise to previously unseen participants. Specifically, the post holder will:

• Develop and maintain reproducible EEG preprocessing, artefact-handling and feature-extraction workflows.
• Implement and compare deep-learning approaches for EEG-based pain prediction, including architectures that capture spatial and temporal structure in the data.
• Develop Riemannian geometry-based methods using EEG covariance representations.
• Investigate cross-participant generalisation, including transfer learning, domain alignment and one-class classification approaches based on pain-free EEG.
• Validate models rigorously using held-out participant data and report performance using appropriate classification metrics.
• Contribute to scientific publications, open-source software, data documentation and the public release of code and curated data.
• Work closely with the Principal Investigator and the clinical collaborators in Norway, contributing to regular interdisciplinary project meetings.

Skills and qualifications required

Applicants will have, or be close to completing, a PhD in computer science, electronic engineering, biomedical engineering, neuroscience, machine learning or a closely related discipline. The following are essential:

• Strong Python programming skills and experience with scientific-computing and machine-learning libraries.
• Experience applying machine learning, including deep learning, to EEG or other high-dimensional biosignals.
• A sound understanding of robust model evaluation, including participant-level cross-validation and testing on independent data.
• The ability to work independently while contributing effectively to a multidisciplinary research team.
• Strong academic writing and spoken communication skills.

The following are desirable:

• Experience with deep-learning architectures for time series or neural signals, such as convolutional or recurrent networks.
• Experience with Riemannian geometry methods for covariance-based signal classification.
• Knowledge of transfer learning, domain adaptation or one-class classification and anomaly detection.
• Familiarity with tools such as MNE-Python, PyTorch, Braindecode or pyRiemann.
• Experience working with clinical data, research governance and open, reproducible research practices.

At the University of Essex, internationalism and diversity is central to who we are and what we do. We are committed to being a cosmopolitan, internationally oriented university that is welcoming to staff and students from all countries, faiths and backgrounds, where you can find the world in one place.

To support this commitment we have our , a staff-led network that promotes and celebrates the rich cultural diversity among Essex staff, and our Colchester campus based , which hosts regular services, meetings and events organised by our chaplains and faith representatives.

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