Research Associate in AI

King's College London; City Of London; On-site; GBP 42,000 - 56,000; Full time

Job summary

King's College London in London invites applications for a Research Associate in AI for infectious diseases. This full-time fixed-term post runs until 28 February 2028.

You will develop and apply advanced AI, machine learning and deep learning methods to large, heterogeneous biomedical datasets, including sequencing and clinical data, to identify drug targets and support ABR research. The role requires a PhD (or near completion) in a relevant field and strong publication record.

Qualifications

• PhD or near completion in CS/AI/Statistics or related field.
• Strong ML/DL background with research publications.
• Experience with AI methods for mining and forecasting from heterogeneous data.

Responsibilities

• Develop and apply AI methods to address research questions.
• Lead analysis of large-scale datasets across multi-omics and clinical data.
• Publish findings and contribute to project milestones.

Skills

Machine learning

Deep learning

Generative AI

Agentic AI

Data mining

Statistical modelling

Transformer architectures

Education

PhD (or near completion) in Computer Science, AI, Statistics, or related field

Job description

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

Essential criteria

1. PhD (or close to completion) in Computer Science, AI, Statistics, or a closely related field
2. Machine learning and deep learning
3. Generative AI (text and numbers; excluding image, audio, video generation)
4. Agentic AI, orchestration, and adaptive systems (continuous feedback, dynamic reasoning, self-optimisation)
5. AI methods for mining, classifying, and forecasting from heterogeneous data sources
6. Spatial-temporal statistics/modelling from heterogeneous data (Bayesian hierarchical models, autoregressive models, random fields
7. Sequence/time-series modelling and language models, particularly transformer architectures
8. Explainable AI methods

Desirable criteria

1. Experience with data analysis in the context of infectious diseases, epidemiology, antimicrobial resistance
2. 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.

Further Information

At King's, we believe that the diversity of our community and a culture that is welcoming, open, inclusive and collaborative, are great strengths of the university.

The Equality Act of 2010 protects the rights of our students and staff and provides a framework to fulfil our duties to eliminate unlawful discrimination, harassment and victimisation and in addition, to advance equality of opportunity and foster good relations between those who share a protected characteristic and those who do not. At times, this will include balancing rights and beliefs that can feel in tension.

We are committed to free speech and to academic freedom, believing that our foundational purpose as a university, is to create spaces where a wide range of ideas, including ideas that are controversial, can be discussed and debated, and where members of our community can express lawful views without fear of intimidation, harassment or discrimination.

When engaging in the robust exchange of ideas, we ask that our community is mindful of our Dignity at King's guidance.

Artificial intelligence (AI) is evolving rapidly, and we recognise its growing role in professional work. Applicants may use AI tools to support preparation of their application, for example to research the role or structure written responses. However, applications must reflect the applicant's own work and experience. AI tools should not be used during interviews or assessment activities unless this has been agreed in advance as a reasonable adjustment. Further guidance on the use of AI in recruitment can be found here .

We reserve the right to close adverts early due to the volume of applications we receive. While the closing date may change, all adverts will close at 23:59 to allow sufficient time for applications to be submitted on that day.