CEREBRIS Research Associate (Biomechanical Motion Analysis & Machine Vision)

About the role

Join the CEREBRIS project, a flagship European Innovation Council (EIC) Pathfinder programme developing a secure, federated and explainable AI ecosystem to transform stroke diagnosis, prognosis and rehabilitation. Working within the Bath Institute for the Augmented Human, alongside Dr Dario Cazzola, Dr Steffi Colyer, Dr Adwait Sharma, Prof Damien Coyle and Dr Jibraan Esoof, you will sit at the interface of post-stroke motion analysis, real-time machine vision and technology translation, helping build the sensing systems that let clinicians objectively track how a patient recovers after brain injury.

In this role you will:

• Develop and validate a markerless camera system for upper limb assessment, building pose estimation models for segmental and fine-grained motion in stroke patients.
• Design hand pose and movement recognition systems suitable for wearable or on-device, low-latency clinical use, and build synthetic data pipelines to augment training data.
• Publish in leading venues such as Nature Communications, ACM CHI or ACM UIST, and contribute to new invention claims within a transdisciplinary, international consortium spanning neurology, AI, robotics and clinical translation.

This role involves intermittent travel to partner sites across Europe in order to work closely with clinical teams to ground your technical development in real patient needs.

About You

We are looking for a motivated, driven and technically strong researcher with a genuine interest in applying computer vision and biomechanics to improve outcomes for people recovering from stroke. You must be comfortable working at the intersection of engineering and clinical research, translating complex motion and vision data into tools that clinicians can use, and confident driving your own research within a transdisciplinary European team.

Essential:

• A PhD in Computer Vision, Biomechanics, Biomedical Engineering, Human Movement Science, or equivalent significant relevant experience and professional qualification.
• Experience with markerless motion capture and 2D/3D human pose estimation.
• Strong programming skills in Python or MATLAB, with experience in kinematic data processing and deep-learning model development.
• A publication record in peer-reviewed venues and the ability to drive independent research within a transdisciplinary project.
• Experience in designing and planning research experiments.
• Experience in data acquisition and recording motion capture data from multiple subjects, and handling large datasets.
• Excellent oral, interpersonal and written communication skills.
• Demonstrated ability to work unsupervised for long periods and deliver results at a professional level.
• Proficiency in IT skills (as appropriate to the discipline).

Desirable:

• Postdoctoral research experience.
• Software skills using OpenSim or equivalent musculoskeletal (MSK) modelling tools.
• Experience in edge AI or on-device inference.
• Experience with self-supervised or minimal-data learning approaches for computer vision.
• Experience conducting research in a neurorehabilitation cohort, including analysing, summarising & presenting findings in written reports.
• A track record of research publications related to research with human participants (conference or journal).
• Experience conducting literature reviews in neuroscience-related research.
• Experience obtaining ethical approval for research.

Further information

This is a full-time (36.5 hours per week) fixed-term role with an expected duration of 30 months.

For an informal discussion regarding the role please contact Jibraan Esoof at je856@bath.ac.uk

We consider ourselves to be a university where difference is celebrated, respected & encouraged. We have an excellent international reputation with staff from over 60 different nations & 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 & students.

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