About Us
King's College London is one of the top 40 universities in the world (QS World University Rankings, 2024) and among the oldest in England. The Department of Population Health Sciences, based at Guy's campus, combines over 160 experts with patients and the public to better understand population health, primary care, social sciences and policy, epidemiology, statistics, and health economics. Our academics use large data sets to evaluate therapies, models of care, and highlight inequalities in access to treatment. We have transformed stroke rehabilitation, linked antidepressant use to weight gain, and uncovered the connections between urban deprivation and long-term health problems.
The School of Life Course & Population Sciences is one of six Schools that make up the Faculty of Life Sciences & Medicine at King's College London. The School unites over 400 experts in women and children's health, nutritional sciences, population health and the molecular genetics of human disease. Our research links the causes of common health problems to life's landmark stages, treating life, disease and healthcare as a continuum. We are interdisciplinary by nature and this innovative approach works: 91 per cent of our research submitted to the Subjects Allied to Medicine (Pharmacy, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research scientists.
About the role
We are seeking a highly motivated and skilled Research Associate in Machine Learning to join our interdisciplinary team on the SOLACE-AI project (https://solaceai.org/). This role is pivotal in developing and implementing advanced machine learning methodologies to create a real-time evidence synthesis tool. SOLACE-AI aims to support policy and humanitarian responses to climate-change health emergencies by providing fast, concise, and locally tailored research evidence.
The SOLACE-AI project, funded by a major grant from the Wellcome Trust, is an ambitious initiative designed to drastically reduce the time, effort, and cost of producing evidence syntheses. By leveraging AI-driven tools, SOLACE-AI will generate on-demand, always up-to-date syntheses that address urgent climate-related health issues. The project will focus on six diverse case studies, including managing cholera risk after flooding in South Africa, addressing poor mental health in displaced communities in Ethiopia, and responding to arbovirus outbreaks globally. These case studies highlight the real-world impact and global reach of SOLACE-AI, aiming to improve decision-making and ultimately enhance the lives of affected communities.
The successful candidate will be responsible for designing, developing, and evaluating AI-driven tools that will form the core of the SOLACE-AI system. You will work closely with a diverse team of researchers, policy makers, and humanitarian organisations to ensure the system meets the needs of all stakeholders. This role involves significant data collection and analysis, rigorous evaluation of machine learning components, and active engagement with stakeholders to refine and improve the system.
You will report to the Principal Investigator and collaborate with other team members to manage project timelines, deliverables, and milestones. Additionally, you will document research processes, prepare reports and publications, and provide training and support to team members and stakeholders on the use of machine learning tools. The post holder will be supported to apply for post-doctoral fellowships. This position offers an exciting opportunity to contribute to a project with significant potential impact on global health and climate change policy.
This is a full-time post (35 hours per week), and you will be offered a fixed term contract until 28th February 2027.
About You
To be successful in this role, we are looking for candidates to have the following skills and experience:
Essential Criteria
1. PhD in Health Informatics *
2. Strong background in machine learning, including experience with large language models (LLMs)
3. Proven experience in developing and implementing machine learning algorithms and models
4. Proficiency in programming languages such as Python, and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch)
5. Experience with data collection, preprocessing, and analysis from diverse sources
6. Strong analytical and problem-solving skills, with the ability to conduct rigorous evaluations of machine learning components
7. Excellent written and verbal communication skills, with a track record of publishing research findings
8. Ability to work collaboratively in an interdisciplinary team and engage with diverse stakeholders
* 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
1. Experience in evidence synthesis or systematic reviews
2. Familiarity with climate change and health-related research
3. Experience with open-source software development and version control systems (e.g., Git)
4. Knowledge of ethical considerations and best practices in AI and machine learning
5. Experience in providing training and support on machine learning tools and methodologies
Further Information
We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.
We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's. We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.
To find out how our managers will review your application, please take a look at our ' How we Recruit ' pages.
We are able to offer sponsorship for candidates who do not currently possess the right to work in the UK. #J-18808-Ljbffr