Research Scientist in Machine Learning for Wearables

About the role

The Research Scientist in Machine Learning for Wearables will develop predictive deep learning models to assess maternal and partner health and behaviour throughout pregnancy, enabling a holistic understanding of health trajectories and personalised interventions.

The post focuses on analysing multimodal data collected from wearable devices (e.g., heart rate, sleep patterns, physical activity) and voice biomarkers to identify patterns linked to maternal health outcomes. The goal is to support personalised health interventions and contribute to the advancement of precision maternal and early childhood care within the EMBRACE research programme, which is led by Professor Josip Car.

Multimodal wearable data will be collected from smartwatches/fitness trackers via continuously monitoring physiological metrics, including heart rate, heart rate variability, sleep patterns, physical activity levels, energy expenditure and so forth. They will be analysed to detect patterns and anomalies correlating with known markers of maternal health, including blood pressure, blood glucose, gestational weight gain, sleep and stress levels. In addition, the project will also aim to analyse voice biomarkers to capture unique vocal features that may reflect pregnant women’s physical and mental health risks and conditions. There will also be opportunities to develop research profile, travel for conferences and presentations, as well as contribute to academic publications.

The post holder is expected to hold a PhD degree in Bioinformatics, Computer Science or other relevant discipline. They will have skills in deep learning for wearable data analysis. Experience of studying health data science and/or machine learning for healthcare would be beneficial.

This is a full-time post (35 hours per week), and you will be offered an a fixed term contract until 06/09/2029.

About you

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

Essential criteria

• PhD in Bioinformatics, Computer Science or other closely related discipline
• Experience in deep learning with a focus on predictive modelling for healthcare applications using wearable data (e.g., physical activity, heart rate, heart rate variability, sleep patterns)
• Sufficient breadth or depth of specialist knowledge in the discipline and of research methods and techniques to work within established research programmes
• Proficiency in signal processing and anomaly detection techniques to interpret physiological and behavioural data
• Research skills, as evidenced by a track record in high-quality academic journal publications and/or contributions to scientific conferences
• Good interpersonal skills, with evidence of networking across teams and complex organisation, along with external partners
• Ability to write research reports and papers accessible to both academic and lay audiences
• Project management skills - ability to initiate, plan, organise, implement and deliver programmes of work to tight deadlines

* Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.

Desirable criteria

• Knowledge of maternal health indicators, such as blood pressure, blood glucose, gestational weight gain, and stress assessment
• Ability or potential to contribute to the development of funding proposals in order to generate external funding to support research projects