Main Purpose of the Job

The post holder will conduct primary AI/ML research in Sheriff Group, developing and advancing the generative AI methodology developed within the CZI rare-disease Digital Twin programme and integrating it into hybrid Digital Twins. Rare diseases present a fundamental machine-learning challenge because patient-level and tissue-specific molecular datasets are sparse and heterogeneous.

The role will develop generative and representation-learning approaches using large public single-cell/multi-omics resources, disease datasets and mechanistic simulations to impute missing molecular information, synthesise responses to intrinsic and extrinsic perturbations and provide predictive components for hybrid Digital Twins.

The post holder will work closely with the Computational Biologist and Mechanistic Modeller to connect learned representations and GenAI predictions with executable models, with scope for methodological research in multimodal learning, perturbation prediction, uncertainty and hybrid mechanistic-AI systems.

We may consider appointing candidates that can operate at a higher level, providing they meet the outlined criteria for the senior position.

Key Relationships

Internal: Sheriff, and work closely with the Computational Biologist and Postdoctoral Research Scientist, along with other members of the Sheriff Group
External: collaborate very closely with Digital Twin team members from EMBL-EBI (Dr Ellen McDonagh and Dr Evangelia Petsalaki, and their team members which includes Digital Twin Postdoc and MultiOmics Data scientist) 

Main Activities & Responsibilities

Percentage

Develop multimodal learning approaches integrating available genotype, phenotype, transcriptomic/single-cell and other molecular information; investigate transfer learning, foundation models and related approaches where scientifically justified for data-scarce rare-disease settings.

20

Develop hybrid mechanistic-AI Digital Twin methods with the Mechanistic Modeller and Computational Biologist, including interfaces between learned and executable models, use of mechanistic simulations for training/augmentation/benchmarking, and AI-based inference of unobserved states, parameters or responses.

20

Establish rigorous benchmarking and validation, including uncertainty quantification/confidence calibration, robustness, generalisation and out-of-distribution behaviour, with biologically meaningful evaluation of predictions.

20

Implement reproducible and scalable AI/ML workflows using GPU/HPC resources and contribute reusable open-source models, code, documentation and Digital Twin software components.

20

Develop and extend generative AI, representation-learning and predictive ML approaches for high-dimensional biological data, including multi-omics imputation, biological-state representation and intrinsic/extrinsic perturbation-response prediction.

For appointment at a higher grade, the post holder must take intellectual and operational leadership of a defined AI/ML workstream, set priorities/milestones and manage technical risks.

10

Prepare manuscripts and conference outputs, present findings and contribute to collaborative research.

For appointment at a higher grade, the post holder must lead major outputs, support grant development and provide methodological guidance/mentoring to junior colleagues.

5

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

5

Education & Qualifications

Requirement

Importance

PhD awarded or expected within 6 months, or equivalent research experience, in Artificial Intelligence, Machine Learning, Computer Science, Computational Biology, Bioinformatics, Statistics, Mathematics, Physics or a related quantitative discipline.

Essential

Demonstrate very high motivation in the project and describe how their skills and expertise align with the aims at posts to make the project successful and support their career progression.

Essential

Specialist Knowledge & Skills

Requirement

Importance

Strong knowledge of modern machine learning and deep learning.

Essential

Excellent Python programming skills and practical experience with PyTorch, JAX, TensorFlow or an equivalent modern ML framework.

Essential

Knowledge and practical experience of generative modelling, representation learning, multimodal learning or related modern AI approaches.

Essential

Strong understanding of rigorous model training/evaluation, generalisation and appropriate validation design.

Essential

Experience with transformers, VAEs, diffusion/generative models, biological foundation models or multimodal architectures.

Desirable

Experience with uncertainty quantification, confidence calibration or out-of-distribution detection.

Desirable

Knowledge of mechanistic modelling, scientific machine learning or hybrid mechanistic-AI approaches.

Desirable

Ability to define and deliver an AI/ML research direction with limited supervision (essential for SC5).

Desirable

Relevant Experience

Requirement

Importance

Demonstrable experience developing, training and evaluating machine-learning or deep-learning models.

Essential

Experience working with high-dimensional scientific data or another complex data domain and developing reproducible research code.

Essential

Evidence of high-quality outputs appropriate to career stage in AI/ML, computational biology or AI for science, through publications and/or significant software/model contributions.

Essential

Evidence of scientific independence, leadership of substantial AI/ML outputs/workstreams and mentoring/collaboration (essential for SC5).

Desirable

Experience with single-cell, transcriptomic, genomic, proteomic or multi-omics data.

Desirable

Experience with perturbation-response modelling, transfer learning or learning in data-scarce settings.

Desirable

Experience with GPU/HPC computing, containers, workflow systems or scalable ML infrastructure.

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 computational methods to interdisciplinary collaborators.

Essential

Ability to work collaboratively in an interdisciplinary team spanning computational biology, AI/ML, mechanistic modelling and biological research.

Essential

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

Essential

Additional Requirements

Requirement

Importance

Attention to detail.

Essential

Promotes equality and values diversity.

Essential

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 its international research reputation and public engagement aims.

Essential

Willingness to work outside standard working hours when required.

Essential

Attention to detail

Essential

Promotes equality and values diversity

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 group develops predictive models and biological Digital Twins that combine mechanistic understanding, AI/ML and high-dimensional biological data. The programme aims to move beyond association-based prediction towards models that can represent biological processes, simulate perturbations and generate testable, interpretable predictions across living systems.

Living in Norfolk

• Visit Norfolk
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Postdoctoral Research Scientist - Generative AI & Digital Twins

Applications are invited for an AI/ML Postdoctoral Research Scientist to join Dr Rahuman Sheriff's AI-integrated Digital Twins Group at the Earlham Institute, based in Norwich, UK.

Background:

The post is part of a Chan Zuckerberg Initiative-funded collaboration between Earlham Institute, Open Targets and EMBL-European Bioinformatics Institute, developing open-source hybrid Digital Twins for rare diseases The programme uses large-scale biological data and mechanistic simulations to address the scarcity and sparsity of rare-disease patient data, and integrates GenAI, other ML approaches and executable mechanistic models. It is co-led by Dr Rahuman Sheriff, Ellen McDonagh (Open Targets/EMBL-EBI) and Evangelia Petsalaki (EMBL-EBI).

The role:

You will continue and advance the programme's GenAI/ML methodology, developing models for biological representation learning, multi-omics imputation and perturbation-response prediction. Potential approaches include generative models, transformers, VAEs, multimodal architectures, transfer learning and biological foundation models where scientifically appropriate.

A major focus will be hybrid mechanistic-AI modelling. You will work with the Mechanistic Modeller and Computational Biologist to connect learned representations and predictions with executable models, use mechanistic simulations for training or validation, and develop uncertainty-aware, robust and biologically meaningful prediction systems for rare-disease Digital Twins.

The CZI programme is co-led by Dr Rahuman, Ellen McDonagh (Open Targets/EMBL-EBI), whose expertise includes rare-disease genetics, pharmacogenomics, target prioritisation and data integration, and Evangelia Petsalaki (EMBL-EBI), whose research includes context-specific cell signalling, causal and phenotype-specific network inference and machine learning. The post holder will work across this interdisciplinary collaboration and with the wider rare-disease and modelling communities

The ideal candidate:

You will have excellent Python and modern deep-learning skills and practical experience developing AI/ML methods. We welcome candidates from AI/ML, computer science and quantitative disciplines as well as computational biology. Biological-data experience is advantageous but strong AI/ML candidates motivated to work on biological problems are encouraged to apply.

For appointment at a higher grade, the postholder must be able to take greater intellectual and operational leadership of a defined AI/ML methodology workstream. We expect evidence of methodological independence, leadership of substantial research outputs and the ability to lead a defined AI/ML workstream with limited supervision.

Additional information:

This is a full-time post for a contract of 2 years, with the possibility for extension depending on funding.

Salary on appointment will be within the range £39,000 to £46,500 per annum depending on qualifications and experience. A starting salary of £40,100 is guaranteed for candidates who can evidence their PhD certificate at appointment; those awaiting confirmation of their PhD award will be appointed at £39,000 until evidence is provided.

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 6 November 2026.