(Senior) Postdoctoral Research Scientist – Biological Foundation Models
Applications are invited for a Postdoctoral Research Scientist or Senior Postdoctoral Research Scientist to join Professor Ke Li's AI for Biology Group in the Research Faculty of the Earlham Institute, based in Norwich, UK.
Background
The AI for Biology Group will be newly established at the Earlham Institute (EI), while building on Professor Ke Li’s established and well-funded research programme in fundamental AI, AI co-scientists, and applications in complex scientific domains such as RNA sequence-function modelling, structure prediction, and inverse design.
The role
We are seeking an ambitious researcher to develop foundation models for biological discovery within a new AI for Biology Group at EI. The successful candidate will create novel AI methods for the pretraining, post-training, adaptation and evaluation of foundation models that support hypothesis generation and experimentally grounded biological discovery.
Building on Professor Ke Li's established research programme in AI for science, the role focuses on developing new AI systems rather than simply applying existing machine learning approaches to biological datasets. The post offers extensive collaboration across the Institute, supporting the development of multimodal biological foundation models, AI-guided design-build-test-learn workflows, and open algorithms, benchmarks and reproducible research.
Ideal candidate
The successful candidate will have a PhD (awarded or expected within 6 months) in Computer Science, AI, Machine Learning, Computational Biology, Mathematics, Statistics, Physics, Electrical Engineering, or a related quantitative discipline.
They will have excellent programming skills in Python and practical experience with modern deep learning frameworks such as PyTorch, JAX or TensorFlow, alongside experience with large-scale model training, GPU/HPC/cloud computing environments, Linux, version control and reproducible research workflows. Experience with large-scale AI training ecosystems, including Hugging Face, DeepSpeed, FSDP, Megatron-LM, Ray or equivalent tools, would be advantageous.
Candidates should demonstrate experience developing, adapting or evaluating original AI algorithms rather than solely applying existing methods. Evidence of high-quality research outputs commensurate with career stage, including publications in leading venues in AI and machine learning, computational biology or AI for science, is essential. Experience with foundation-model pre-training in natural language processing, computer vision or biological domains, including genomics, transcriptomics, proteins, DNA or RNA, would be highly beneficial.
Additional information
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