Job Description

Job number

MED05463

Faculties

Faculty of Medicine

Departments

Department of Brain Sciences

Salary or Salary range

49,017 - 57,472 per annum

Location/campus

London campuses

Contract type work pattern

Part time - Fixed term

Posting End Date

1 Dec 2025

About the role

------------------


The Department of Brain Sciences at Imperial College London is looking to fill a Research Associate position to contribute to our research in autonomic neurosciences and syncope, specifically applying machine learning to a large and diverse, curated clinical dataset.



The candidate should have a PhD or MSc in a relevant field such as Neuroscience, Cardiovascular Science, Computer Science, Mathematics, or related disciplines with a focus on machine learning, deep learning or data science for clinical applications.



The postholder will become a member of the research group in the Department of Brain Sciences working with Dr Melanie Dani, Professor Payam Barnaghi and Dr Gregory Scott. They will be expected to carry out research independently, submit publications to refereed journals and attract external research funding.



This work will focus on interrogating and applying machine learning to one of the largest and most diverse clinical syncope datasets (tilt table tests) to our knowledge, and thus answering key questions about the physiology and mechanisms of syncope and dysautonomia, and contribute to better classifying and naming this diverse set of disorders.



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 candidate will play a central role in developing the scientific and machine learning work within the Department of Brain Sciences.



The post will suit a highly motivated candidate who is interested in addressing real-world challenges and creating end-to-end solutions.

What you would be doing

---------------------------


The research will involve working with partially labelled data, performing unstructured and semi-structured data analysis, and applying supervised and unsupervised learning approaches to develop decision support and predictive models. The technical work will also require integration, verification and validation of the designed solutions using existing clinical study data. Experience in software development and programming and practical applications of machine learning in clinical data analysis will be highly desirable. The candidate will be supported in their career development

What we are looking for

---------------------------


PhD in cardiovascular science, neuroscience, computer science or a closely related discipline, or equivalent research, industrial or commercial Knowledge of clinical studies, cardiovascular science or neuroscience Knowledge of research methods and statistical procedures Practical experience within a research environment A record of high-quality publications in international peer-reviewed journals Ability to organise your own work with minimal supervision A creative approach to problem-solving; a "doer".

What we can offer you

-------------------------


A unique opportunity to work on an exciting project that can transform our understanding and management of major clinical problems Join a highly multi-disciplinary lab and a world-leading institution Progress your academic career with support from the lab and department, in an institution with dedicated support for research associates.

Further information

-----------------------


This is a Fixed term (5 years) part time role based at our London Campuses.



Please contact Dr Melanie Dani (melanie.dani@nhs.net or m.dani@imperial.ac.uk) for further information.

Available documents

-----------------------


Attached documents are available under links. Clicking a document link will initialize its download.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.



If you encounter any technical issues while applying online, please don't hesitate to email us at support.jobs@imperial.ac.uk. We're here to help.

About Imperial

Welcome to Imperial, a global top ten university where scientific imagination leads to world-changing impact.



Join us and be part of something bigger. From global health to climate change, AI to business leadership, here at Imperial we navigate some of the world's toughest challenges. Whatever your role, your contribution will have a lasting impact.



As a member of our vibrant community of 22,000 students and 8,000 staff, you'll collaborate with passionate minds across nine London campuses and a global network.



This is your chance to help shape the future. We hope you'll join us at Imperial College London.

Our Culture

We work towards equality of opportunity, to eliminating discrimination, and to creating an inclusive working environment for all. We encourage applications from all backgrounds, communities and industries, and are committed to employing a team that has diverse skills, experiences and abilities. You can read more about our commitment on our webpages.



Our values are at the root of everything we do and everyone in our community is expected to demonstrate respect, collaboration, excellence, integrity, and innovation.