Research Associate in AI for Evidence Synthesis

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.

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

• PhD in Health Informatics, Computer Science, Machine Learning, Health Data Science, or a related field *
• Strong background in machine learning, including experience with large language models (LLMs)
• Proven experience in developing and implementing machine learning algorithms and models
• Proficiency in programming languages such as Python, and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch)
• Experience with data collection, preprocessing, and analysis from diverse sources
• Strong analytical and problem-solving skills, with the ability to conduct rigorous evaluations of machine learning components
• Excellent written and verbal communication skills, with a track record of publishing research findings
• 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

• Experience in evidence synthesis or systematic reviews
• Familiarity with climate change and health-related research
• Experience with open-source software development and version control systems (e.g., Git)
• Knowledge of ethical considerations and best practices in AI and machine learning
• Experience in providing training and support on machine learning tools and methodologies

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