(Senior) Postdoctoral Research Scientist – Biological Foundation Models in Norwich

Main Purpose of the Job

The post holder will conduct primary research in the AI for Biology Group to develop large-scale multimodal foundation models. The role will focus on original AI methods for pretraining, post-training, adaptation and evaluation across biological modalities, including DNA and RNA sequences, genomics, transcriptomics, single-cell and spatial omics, imaging, phenotypic and perturbation data. The aim is to build new AI systems that can represent biological mechanisms across scales and enable experimentally grounded discovery.

The post holder will help build the representation and prediction layer of the Generative Digital Biology programme: models that can connect molecular, regulatory, cellular, tissue and organismal scales, support biological hypothesis generation, and enable downstream experimental design. This will be a highly collaborative role embedded across EI.

Key Relationships

• INTERNAL: Reporting to Professor Ke Li, the post holder will work closely with the AI for Biology Group and collaborate across EI's research programmes, National Bioscience Research Infrastructures and technology platforms.
• EXTERNAL: The post holder will interact with UK and international collaborators in AI, machine learning, computational biology, genomics, single-cell and spatial biology, engineering biology, plant science, human health and therapeutic discovery.

Main Activities & Responsibilities

• Develop original multimodal foundation model architectures for biological data.
• Design and implement large-scale pretraining, post-training, fine-tuning, adaptation and evaluation pipelines for biological AI models.
• Work with EI colleagues to help define AI-ready biological data atlases, metadata standards, model and dataset documentation, and foundation-model-ready benchmarks.
• Build transferable representations that connect molecular, regulatory, cellular, tissue and organismal scales.
• Collaborate with various EI groups to identify biological use cases and translate model outputs into experimentally useful hypotheses or designs.
• Develop benchmark tasks, ablation studies, uncertainty estimates and robustness/generalization analyses.
• Prepare manuscripts and conference papers for leading venues; present findings internally, nationally and internationally.
• Contribute to research proposals, grant applications, open-source software, model cards, dataset documentation and good research practice.
• Any other duties commensurate with the nature of the role.

Person Profile

• Education & Qualifications: PhD (awarded or expected within 6 months) in a related quantitative discipline.
• Specialist Knowledge & Skills: Excellent programming skills in Python and practical experience with deep learning frameworks.
• Experience with large-scale model training, GPU/HPC/cloud computing, Linux, version control and reproducible research workflows.
• Understanding of biological data types such as DNA/RNA sequences, genomics, transcriptomics, etc.
• Demonstrable experience developing or leading foundation-model research in biology or another complex scientific domain.
• Ability to develop and deliver a research direction with limited supervision.
• Evidence of high-quality outputs appropriate to career stage in AI/ML, computational biology, bioinformatics or AI for science.
• Excellent written and verbal communication skills.