Overview
Research Associate in AI – Strand, London, WC2R 2LS
About us
King’s College London is a world leading research-intensive university with a strong international profile and a long-standing commitment to addressing major societal and global challenges.
This role is based within the Faculty of Life Sciences & Medicine, in the School of Immunology & Microbial Sciences at the Department of Infectious Diseases, which brings together expertise across microbiology, immunology, clinical sciences, data science and artificial intelligence. The Department offers a highly interdisciplinary and collaborative environment, with strong links to clinical partners and access to outstanding research infrastructure and cross faculty initiatives.
About the role
This is an exciting project Supported by the MRC. Antibiotic resistance (ABR) is one of the biggest public health challenges of our time. In 2019, 1.27 million deaths were attributed to antibiotic resistant bacteria. Of particular concern are the infections caused by K. pneumoniae, globally, the third leading pathogen associated with deaths (250,000) attributed to any antibiotic resistant infection. Not surprisingly, the World Health Organization (WHO) has singled out K. pneumoniae as global health threat. Worryingly, the therapeutic options for the treatment of Klebsiella infections have significantly narrowed due to the spread of strains with combined resistance to fluoroquinolones, third-generation cephalosporins and aminoglycosides. This project will develop an AI approach to discover potentially druggable K. pneumoniae proteins and to identify lead drugs inhibiting the target by a deep learning approach fed with 3D modelling. The proposed AI approach will foster an innovative integrated analysis of large-scale multiple ‘omics, genome-scale metabolic models, and high-throughput phenotyping (antibiotic susceptibility testing, virulence, and host immunity responses) to identify, prioritize and validate new druggable targets while dissecting their contribution to ABR and K. pneumoniae infection biology. Finally, we will provide pre-clinical evidence demonstrating the therapeutic potential of the new drugs alone and in combination with antibiotics.
The research associate will be responsible for developing, implementing and applying artificial intelligence, machine learning, deep learning and data-mining methods to address complex research questions. They will lead the analysis of large-scale, heterogeneous datasets, including sequencing, sensor, biological and medical data, and ensure robust, reproducible computational workflows.
The postholder will be responsible for developing and applying AI methods across the following areas, demonstrated through scientific publications or other academic/industrial achievements:
- Generative AI (text and numerical data; excluding image, audio, video generation)
- Agentic AI, orchestration, and adaptive systems (continuous feedback, dynamic reasoning, self-optimisation)
- AI methods for mining, classifying, and forecasting from heterogeneous data sources
- Spatial-temporal statistics/modelling from heterogeneous data (Bayesian hierarchical models, autoregressive models, random fields)
- Sequence/time-series modelling and language models, particularly transformer architectures
- Explainable AI methods
- Causal AI methods
- Machine learning
This is a full-time post 35 Hours a week, and you will be offered a fixed term contract until 28th February 2028.
Research staff at King’s are entitled to at least 10 days per year (pro-rata) for professional development. This entitlement, from the Concordat to Support the Career Development of Researchers, applies to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows and AEP equivalent up to and including grade 7. Visit the Centre for Research Staff Development for more information.
About you
To be successful in this role, we are looking for candidates to have the following skills and experience:
Essential criteria
- PhD (or close to completion) in Computer Science, AI, Statistics, or a closely related field
- Machine learning and deep learning
- Generative AI (text and numbers; excluding image, audio, video generation)
- Agentic AI, orchestration, and adaptive systems (continuous feedback, dynamic reasoning, self-optimisation)
- AI methods for mining, classifying, and forecasting from heterogeneous data sources
- Spatial-temporal statistics/modelling from heterogeneous data (Bayesian hierarchical models, autoregressive models, random fields
- Sequence/time-series modelling and language models, particularly transformer architectures
- Explainable AI methods
Desirable criteria
- Experience with data analysis in the context of infectious diseases, epidemiology, antimicrobial resistance
- Experience in the development of diagnostics and /or therapeutics solutions, drug-discovery
Downloading a copy of our Job Description
Full details of the role and the skills, knowledge and experience required can be found in the Job Description document, provided at the bottom of the page. This document will provide information of what criteria will be assessed at each stage of the recruitment process.
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.
IMPORTANT: Before applying for this role, please make sure you have the right to work in the country where the role is based. Unless it clearly stipulates within in the job advert above that the hiring company is looking to or able to sponsor applicants it is deemed that the hiring employer will only consider applications from those able to comply with and work in the country where the role is based.
