CEREBRIS Research Associate (AI for Neurotechnology

About the role

Join the
CEREBRIS project, a flagship
European Innovation Council (EIC) Pathfinder programme developing next-generation AI and neurotechnology to transform stroke diagnosis, prognosis and rehabilitation. Working within the
Bath Institute for the Augmented Human
, you will collaborate with leading researchers and an international consortium to deliver cutting-edge translational research.

• Develop and apply advanced AI methods, including multimodal foundation models and deep learning, to analyse complex neurotechnology and clinical datasets.
• Work with multimodal data including EEG, fNIRS, MRI, movement analysis and clinical assessments to develop explainable AI for stroke recovery.
• Publish high-impact research, contribute to international collaborations, and present your work at leading conferences.
• Help shape the future of AI-driven neurotechnology within a multidisciplinary team spanning computer science, engineering, neuroscience and clinical research.

As a member of Research Staff at the University of Bath, you will be encouraged to take up a minimum of 10 days professional development pro-rata per year.

A lower grade offer may be made (Grade
6) with comparable reduction in responsibilities & amendment in job title to Research Assistant, if a suitable applicant cannot be found to fill the Grade 7 position. Applicants will need to be within 6 months of their course completion date and will be promoted to Research Associate once their PhD has been awarded.

We are looking for a motivated researcher with a passion for applying AI to solve real-world healthcare challenges. You will have strong technical skills, experience with neurophysiological data, and enjoy working collaboratively within a multidisciplinary research environment.

• A PhD (or equivalent experience and professional qualification) in Artificial Intelligence, Computer Science, Machine Learning, Biomedical Engineering, Computational Neuroscience or a closely related discipline.
• Experience developing AI or machine learning methods using frameworks such as PyTorch and/or Tensor Flow.
• Hands-on experience acquiring, processing & analysing EEG and/or functional near-infrared spectroscopy (fNIRS) data.
• Experience developing robust signal processing and/or machine learning pipelines for complex datasets.
• Evidence of research excellence, including publications or the potential to publish in high-quality peer-reviewed journals.
• Excellent programming, analytical, written and verbal communication skills.
• Ability to work independently and collaboratively within a multidisciplinary research team.
• Excellent organisational skills with the ability to manage competing priorities and meet project deadlines.
• A commitment to conducting research to the highest professional & ethical standards.

• Experience with multimodal AI, foundation models, generative AI or representation learning.
• Experience integrating neurophysiological data with neuroimaging, movement or clinical datasets.
• Experience with GPU-accelerated computing or high-performance computing environments.
• Experience contributing to research proposals, supervising students or working within international collaborative research projects.

This is a full-time (36.5 hours per week) fixed-term role lasting approximately 36 months.

We consider ourselves to be a university where difference is celebrated, respected and encouraged.

We have an excellent international reputation with staff from over 60 different nations and have made a positive commitment towards gender equity and intersectionality receiving a Silver Athena SWAN award.

We truly believe that diversity of experience, perspectives, and backgrounds will lead to a better environment for our employees and students and encourage applications from all genders, backgrounds, and communities, particularly from under-represented groups, and value the positive impact that will have on the university.

We are committed to maintaining a safe and secure environment for our students, staff, and community.