Research Associate in AI

Overview

As a Research Associate, you will develop and apply AI, machine learning, and data-mining methods to address complex questions in infectious diseases and antimicrobial resistance. You will lead analysis of large, heterogeneous datasets and ensure robust, reproducible computational workflows. The role sits in a highly interdisciplinary team and contributes to high-impact, publication-worthy work. This fixed-term post supports a BBSRC-funded project focused on the gut microbiome, infection, and resistance dynamics. You will work closely with clinical partners and cross-faculty groups to advance understanding and solutions.

Pay / Benefits

• professional development entitlement (10 days/year pro-rata)
• London weighting
• collaborative research environment

Responsibilities

• Develop, implement, and apply AI/ML/deep learning methods to address research questions
• Lead analysis of large-scale, heterogeneous datasets (sequencing, sensor, medical data)
• Ensure robust, reproducible computational workflows and pipelines
• Develop and apply AI methods across generative AI, agentic AI, and AI for mining, classification, and forecasting
• Apply spatial-temporal statistics/modelling on heterogeneous data using Bayesian, autoregressive, and random field approaches
• Work with sequence/time-series modelling and transformer-based language models
• Utilise Explainable AI and causal AI methods to interpret results
• Contribute to dissemination through scientific publications and academic outputs

Key requirements

• 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
• AI methods for mining, classifying, and forecasting from heterogeneous data sources
• Spatial-temporal statistics/modelling from heterogeneous data
• Sequence/time-series modelling and language models, particularly transformer architectures
• Explainable AI methods

• Machine learning
• Deep learning
• Generative AI
• Agentic AI
• AI for heterogeneous data
• Spatial-temporal modelling

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