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.

Potentially ranging from hundreds of millions to tens of billions of parameters where scientifically justified, to underpin the Generative Digital Biology programme.

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 successful candidate will join at a rare moment: early enough to help shape a new programme at EI, but with strong technical foundations, prior publications, existing collaborations and a clear research trajectory already in place. The aim is not simply to apply existing machine learning tools to biological datasets, but to build new AI systems that can represent biological mechanisms across scales and enable experimentally grounded discovery.

The ambition is to move beyond static biological representation learning towards predictive, transferable and experimentally grounded models of living systems. 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. The post holder will work with EI colleagues and platforms to develop AI-ready biological data resources and benchmarks, including
• with BioFAIR and ELIXIR-UK on FAIR, interoperable and foundation-model-ready data;
• with the Cellular Genomics programme and Single-cell and Spatial Analysis platform on single-cell and spatial omics;
• and with the Earlham Biofoundry and engineering biology colleagues on model-guided experimental design.

Together, these capabilities make EI a distinctive environment for building biological foundation models that are both technically ambitious and experimentally grounded.

The post holder will be expected to lead high-quality research outputs, publish in leading AI, machine learning, computational biology and AI-for-science venues, contribute to open and reproducible models, data and software resources, and support future competitive grant applications to UKRI, EPSRC, BBSRC, Wellcome, ERC and related funders.

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. Key internal relationships are expected to include BioFAIR, ELIXIR-UK, and Open and FAIR Data colleagues; Research e-Infrastructure; the Cellular Genomics programme; the Single-cell and Spatial Analysis platform; Earlham Biofoundry and engineering biology colleagues; Transformative Genomics; High-Performance Sequencing; and relevant EI scientific groups working on plants, microbes, biodiversity, health, genomics and data-intensive bioscience. The role is intended to help make the AI for Biology group a collaborative AI engine for EI, supporting AI-ready data atlases, foundation-model-ready benchmarks and model-guided experimental design across the Institute.

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. External collaborations may include academic, clinical, public-sector, infrastructure and industry partners where appropriate.
 

Main Activities & Responsibilities

Percentage

Develop original multimodal foundation model architectures for biological data, including sequence, genomics, transcriptomics, single-cell and spatial omics, imaging, phenotype and perturbation modalities.

For appointment at SC5, take intellectual and operational leadership of a defined foundation-model workstream, set scientific priorities and milestones, manage technical risks, and deliver the work with limited supervision (essential for SC5)

25

Design and implement large-scale pretraining, post-training, fine-tuning, adaptation and evaluation pipelines for biological AI models using GPU, HPC and/or cloud computing and reproducible research workflows.

20

Work with EI colleagues, including BioFAIR, ELIXIR-UK, Open and FAIR Data and Research e-Infrastructure teams, to help define AI-ready biological data atlases, metadata standards, model and dataset documentation, and foundation-model-ready benchmarks.

15

Build transferable representations that connect molecular, regulatory, cellular, tissue and organismal scales, and evaluate their utility for prediction, perturbation response and biological discovery.

15

Collaborate with Cellular Genomics, Single-cell and Spatial Analysis, Earlham Biofoundry, engineering biology and other EI groups to identify biological use cases and translate model outputs into experimentally useful hypotheses or designs.

For appointment at SC5, coordinate the relevant interdisciplinary collaboration and ensure that model outputs are translated into a coherent programme of experimentally actionable hypotheses or designs (essential for SC5).

10

Develop benchmark tasks, ablation studies, uncertainty estimates and robustness/generalization analyses to assess biological validity, transferability and downstream utility.

5

Prepare manuscripts and conference papers for leading AI, machine learning, computational biology and life science venues; present findings internally, nationally and internationally.

For appointment at SC5, lead the preparation and submission of major research outputs and represent the work in relevant external forums (essential for SC5)

5

Contribute to research proposals, grant applications, open-source software, model cards, dataset documentation and good research practice, including responsible data handling and reproducibility.

For appointment at SC5, make substantive contributions to grant development and provide scientific or technical guidance to junior researchers or students (essential for SC5).

5

As agreed with line manager, any other duties commensurate with the nature of the role.

Education & Qualifications

Requirement

Importance

PhD (awarded or expected within 6 months) in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, Mathematics, Statistics, Physics, Engineering or a related quantitative discipline

Essential

Specialist Knowledge & Skills

Requirement

Importance

Excellent programming skills in Python and practical experience with PyTorch, JAX, TensorFlow or equivalent deep learning frameworks, ideally including large-scale model training ecosystems such as Hugging Face, DeepSpeed, FSDP, Megatron-LM, Ray or equivalent tools

Essential

Experience with large-scale model training, GPU/HPC/cloud computing, Linux, version control and reproducible research workflows

Essential

Understanding of biological data types such as DNA/RNA sequences, genomics, transcriptomics, single-cell, spatial, imaging, phenotypic or perturbation datasets

Desirable

Experience with FAIR data, metadata standards, biological data atlases, benchmark datasets, model cards, dataset documentation or reusable ML resources

Desirable

Demonstrable experience developing or leading foundation-model research in biology or another complex scientific domain (essential for SC5)

Desirable

A strong track record of independent or semi-independent research in machine learning, AI, computational biology, bioinformatics, or a closely related field (essential for SC5)

Desirable

Ability to develop and deliver a research direction with limited supervision, including project planning, collaboration and communication with interdisciplinary partners (essential for SC5)

Desirable

Relevant Experience

Requirement

Importance

Evidence of high-quality outputs appropriate to career stage in AI/ML, computational biology, bioinformatics or AI for science, demonstrated through peer-reviewed publications and/or significant open-source models, datasets or benchmark contributions

Essential

Experience designing, adapting or evaluating original AI algorithms rather than only applying existing tools

Essential

Experience working with large biological datasets, multi-omics data, single-cell/spatial data or cross-modal biological prediction tasks

Desirable

Experience contributing to externally funded research projects, open-source software, benchmark datasets, data/model resources or collaborative research consortia

Desirable

Clear evidence of leading high-quality research outputs, for example first-author publications, substantial contributions to major papers in top AI conferences (essential for SC5)

Desirable

Ability to support junior group members, contribute to collaborative projects, and help develop future publications and grant applications (essential for SC5)

Desirable

Interpersonal & Communication Skills

Requirement

Importance

Ability to work independently, use initiative, solve complex research problems and deliver against agreed milestones

Essential

Excellent written and verbal communication skills, including the ability to communicate AI methods to biological collaborators

Essential

Ability to work collaboratively in an interdisciplinary team spanning AI, genomics, computational biology, data infrastructure and experimental biology

Essential

Good interpersonal skills, with the ability to work well as part of a team

Essential

Additional Requirements

Requirement

Importance

Commitment to reproducible, open and responsible research

Essential

Willingness to embrace the expected values and behaviours of all staff at the Institute, ensuring it is a great place to work

Essential

Able to present a positive image of self and the Institute, promoting both the international reputation and public engagement aims of the Institute

Essential

Willingness to work outside standard working hours when required

Essential

Promotes equality and values diversity

Essential

Motivation to extend existing foundation-model research into new biological modalities, organisms, benchmarks and experimentally grounded discovery workflows

Essential

Attention to detail

Essential

Earlham Institute

About the Earlham Institute
The Earlham Institute harnesses the power of data-driven biology to accelerate solutions for health, biodiversity, and food security. Based at Norwich Research Park, the Earlham Institute is one of eight institutes strategically funded by BBSRC.

Our science combines world-class technology, interdisciplinary expertise, and training and development across genomics, engineering biology and data science, to decode the scale and complexity of living systems.

We believe we can achieve more if we work together. That's why we collaborate with the global science community and industry partners, while also inspiring the next generation of scientists and technical specialists.

Our Science
Earlham Institute scientists specialise in developing and testing the latest tools and approaches needed to decode living systems and make biological predictions.

We are home to state-of-the-art facilities and technology, creating a unique combination of expertise and infrastructure.

We have dedicated laboratories for genome sequencing, single-cell analysis, engineering biology, and large-scale automation; as well as one of the largest supercomputing facilities for life science research in Europe.

Our Advanced Training team also provides access to specialised scientific training to upskill the next generation of research and technical staff.

Our Culture
Our collegiate and innovative research environment comes with significant support, including a commitment to your professional development, research and administrative assistance, and opportunities to build collaborations with scientists and industry on the Norwich Research Park, across the UK, and internationally.

We are committed to building and maintaining a workplace that treats every individual with dignity and respect. By taking an active approach to fostering inclusivity, diversity, equality and accessibility, we empower our community to achieve more.

The Institute is also home to talented technical and operational staff, whose invaluable contributions enable our science to have the maximum impact. We aim to recognise, reward, and develop all staff and students so that every individual feels able to achieve their best with us.

We work hard to nurture an engaged and positive workplace, centred on core values that include openness, technical excellence, and collaboration. We attract staff from around the world who contribute to - and benefit from - an environment that enables them to deliver world-class science alongside a supportive and social community.

For more information about working at the Earlham Institute, please click here.

Further Information:

• Our Vision and Mission
• Our Strategy
• Our Culture
• Life at Earlham Institute

Department

Research Faculty

Group Details

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, biological foundation models and AI-driven scientific discovery. Recent work from the group spans foundation model pretraining and adaptation [1]-[3], genomic model evaluation and interpretation [4]-[6], RNA inverse design [7][8], and AI co-scientist systems [9][10]. The group’s broader methodological foundations include data-driven, multi-objective and multi-fidelity optimisation, reinforcement learning, Bayesian decision-making and automated scientific discovery, with applications across RNA design, software engineering, renewable energy and other complex scientific domains. Further information is available at https://colalab.ai/.

At EI, the group will extend these foundations into a broader Generative Digital Biology (GDB) programme, developing multimodal, cross-organism and experimentally grounded AI systems that can learn from biological data, support hypothesis generation, guide experimental design and accelerate discovery across EI’s research programmes and technology platforms.

The successful candidate will join a group with both ambition and infrastructure. The group is supported by substantial AI compute for frontier model development, including more than 70 dedicated GPUs, currently comprising 12 NVIDIA H200 and 8 NVIDIA H100 GPUs, alongside further capacity through the Norwich Data Centre and routes to national-scale AI compute such as Isambard-AI. This environment will support large-scale model pretraining, post-training, and adaptation, generative design, AI-agent systems and rigorous biological model evaluation. Collaboration with the Earlham Biofoundry will create routes for model-guided experimental design and, where appropriate, closed-loop self-driving laboratory workflows supported by established lab automation.

Living in Norfolk

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(Senior) Postdoctoral Research Scientist – Biological Foundation Models

Applications are invited for a Postdoctoral Research Scientist (SC6) or Senior Postdoctoral Research Scientist (SC5) 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. Further information is available at https://colalab.ai/.

At EI, the group will develop an ambitious Generative Digital Biology (GDB) programme, developing multimodal, cross-organism and experimentally grounded AI systems that can learn from biological data, support hypothesis generation, guide experimental design and accelerate discovery across EI’s research programmes and technology platforms.

The successful candidate will have access to substantial AI compute, including in-house state-of-the-art H200 GPU servers, alongside further capacity through the Norwich Data Centre and access to national-scale AI compute through Isambard-AI. This provides a strong environment for frontier AI development and evaluation. Collaboration with the Earlham Biofoundry provides routes for model-guided experimental design and, where appropriate, closed-loop self-driving laboratory workflows supported by established lab automation.

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.

This is an exciting opportunity to help shape a frontier AI for Biology programme, publish high-impact research, and contribute to future funding applications.

Appointment will be made at SC6 (Postdoctoral Research Scientist) or SC5 (Senior Postdoctoral Research Scientist), according to the candidate’s experience and evidence against the published criteria.

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.

For appointment at SC5, candidates will additionally be expected to demonstrate experience leading foundation-model research in biology or another complex scientific domain; a strong track record of independent or semi-independent research; the ability to develop and deliver a research direction with limited supervision; clear evidence of leading high-quality outputs; and the ability to coordinate collaborations, support junior researchers, and contribute substantively to publications and grant applications.

Additional information:

Salary on appointment will be within the range £39,000 to £46,500 per annum depending on qualifications and experience for the SC6 level post, and £47,450 to £52,560 per annum for candidates who meet the SC5 level criteria. This is a full-time post for a contract of 36 months, with possible extension.

This role meets the criteria for a visa application, and we encourage all qualified candidates to apply.  Please contact the Human Resources Team if you have any questions regarding your application or visa options.

As a Disability Confident employer, we guarantee to offer an interview to all disabled applicants who meet the essential criteria for this vacancy.

The closing date for applications will be 20 August 2026.