Research Assistant in Machine Learning for Medicine

Find out more about this role by reading the information below, then apply to be considered.

Job Type: Full-Time.

Starting Salary: £43003 - £46297 per annum + 43003 plus benefits

To find out more about the job please click the ‘apply for job’ button to be taken to Imperial job site

About the role

We are recruiting a Research Assistant position to support research aimed at identifying earlier and more accessible biomarkers for Parkinson's disease, including digital biomarkers. The position is funded by UKRI and will be supported by a Global Talent Visa. The ideal candidate should have a relevant MSc in Neuroscience, Computer Science, Mathematics, or related disciplines with a focus on machine learning, deep learning, or data science. The postholder will become a member of the research group in the Department of Brain Sciences, working with Dr. Cynthia Sandor, Dr. Shlomi Haar, and Professor Payam Barnaghi. The research will focus on analyzing wearable data collected from deeply phenotyped cohorts of patients affected by Parkinson's disease (PPMI), various biobanks (UK Biobank, All of Us), and different clinical cohorts from collaborators. More information about the research programme and the Sandor, Haar and Barnaghi research group is available at:

What you would be doing

We are looking for a creative and enthusiastic researcher who can take on a challenging role with considerable scope for independent scientific achievement and personal growth. The successful candidates will play a central role in developing the neuroscience and machine learning work within the Department of Brain Sciences.

The post will suit a highly motivated candidate who is interested in the development and application of computational approaches to identify earlier digital biomarkers in Parkinson's disease. The applicant will collaborate with various research groups within the Dementia Research Institute and Imperial's Department of Brain Sciences. Candidates will be supported in their career development. This post aims to derive early biomarkers from data collected using wearable devices. We will analyze wearable data collected from deeply phenotyped cohorts of patients affected by Parkinson's disease (PPMI), various biobanks (UK Biobank, All of Us), and different clinical cohorts from collaborators.

What we are looking for

• You must possess a master's degree (or equivalent) in Neuroscience, Computer Science, Mathematics, or a related field with an emphasis on machine learning, deep learning, or data science.
• You should be proficient in machine learning and computational modelling.
• You need a solid understanding of research methodologies and statistical procedures.
• You should have experience with machine learning frameworks and Python.
• You need to be familiar with wearable device APIs, SDKs, and platforms, such as those provided by Fitbit, Garmin, or Apple Health.
• You should understand how to handle time-series data and be skilled in signal processing techniques relevant to wearable device data.
• You must be able to parse and clean raw data from various sensors, such as accelerometers and heart rate monitors.

What we can offer you

• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• Grow your career: Gain access to Imperial's sector-leading dedicated career support for researchers as well as opportunities for promotion and progression

• Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).

Further information

This is a full time and a fixed term (12 months) role based at the White City Campus.

If you require any further details on the role please contact: Dr Cynthia Sandor -