Senior Research Officer

University of Essex; Colchester; Presencial; GBP 35.000 - 43.000; Jornada completa

Descripción de la vacante

University of Essex in Colchester is seeking a researcher to advance computational analysis for the PaiNeuro project, developing machine-learning methods to separate pain-related and pain-free states in clinical EEG. You will implement reproducible preprocessing and modeling on held-out data, and contribute to publications and open-source software.

The role requires a PhD in a related field, strong Python/ML skills, and experience with EEG data.

Formación

• PhD in computer science, electronic engineering, biomedical engineering, neuroscience, machine learning or a closely related discipline.
• Strong Python programming and ML library experience.
• Experience applying ML, including deep learning, to EEG or high-dimensional biosignals.
• Robust model evaluation including participant-level cross-validation and independent testing.
• Ability to work independently in a multidisciplinary team.
• Strong academic writing and spoken communication skills.

Responsabilidades

• Develop and maintain reproducible EEG preprocessing, artefact-handling and feature-extraction workflows.
• Implement and compare deep-learning approaches for EEG-based pain prediction with spatial and temporal architectures.
• Develop Riemannian geometry-based methods using EEG covariance representations.
• Investigate cross-participant generalisation, including transfer learning and domain alignment.
• Validate models using held-out participant data and report performance with suitable metrics.
• Contribute to publications, open-source software, data documentation and public data/code releases.
• Collaborate with the PI and clinical collaborators in Norway, attending interdisciplinary meetings.

Conocimientos

Python

Machine learning

EEG

Cross-validation

Academic writing

Educación

PhD in computer science / engineering / neuroscience

Herramientas

MNE-Python

PyTorch

Braindecode

pyRiemann

Descripción del empleo

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.

£38,784 per annum