Post Details
Job Title: Senior Computational Biologist - AI & Rare Disease Digital Twins
Post Number: 1006213
Closing Date: 6 Dec 2026
Grade: SC5
Starting Salary: £47,450 - £58,000
Hours per week: 37
Project Title: Development of Digital Twins for modelling rare diseases and exploring potential applications
Months Duration: 24
Job Description
Main Purpose of the Job
The post holder will conduct primary research in the Sheriff Group and provide a central biological and computational integration role within the Chan Zukerberg Initiative-funded rare-disease Digital Twin program. The role combines strong bioinformatics and omics expertise with practical AI/ML and systems-modelling capability.
The post holder will process and integrate patient-derived transcriptomic, single-cell and other multi-omics/phenotypic data; map these data onto reusable Digital Twin models; integrate mechanistic and AI/ML components; construct and personalise patient- or patient-group-specific Digital Twins; and lead their biological application, benchmarking and validation. The post holder will also have substantial scope to develop hybrid mechanistic-AI methodology rather than acting solely as a data-analysis specialist and will be expected to have greater intellectual and operational ownership of a defined Digital Twin integration/validation workstream, with limited supervision.
The post holder will closely collaborate with the groups of Ellen McDonagh (Open Targets/EMBL-European Bioinformatics Institute), 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 also work across this interdisciplinary collaboration and with the wider rare-disease and modelling communities.
Key Relationships
Internal: Sheriff, and work closely with the Postdoctoral Research Scientists, 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
| Activity | Percentage |
|---|---|
| Process, harmonise, analyse and integrate patient-derived bulk/single-cell transcriptomic, multi-omics, genotype, phenotype and related biological datasets; perform pathway, network, cell-state and disease-context analyses needed for Digital Twin construction. | 25 |
| Construct and personalise hybrid Digital Twins by mapping patient molecular measurements and derived features onto reusable Digital Twin models and integrating multi-omics data with mechanistic and AI/ML components. The post holder will take intellectual and operational leadership of a defined integration workstream, set priorities/milestones and manage scientific risks. | 25 |
| Develop and evaluate hybrid modelling approaches that combine omics-derived representations, AI/ML predictions and executable mechanistic models; work with the Mechanistic Modeller and AI/ML Scientist on interfaces and end-to-end Digital Twin methodology. | 20 |
| Apply Digital Twins to rare-disease biological questions, including healthy-versus-disease comparison, patient stratification and in-silico perturbation; interpret results and generate biologically meaningful hypotheses. | 10 |
| Establish biological and computational validation/benchmarking strategies; compare predicted and observed molecular/phenotypic outcomes and evaluate robustness, generalisation and biological plausibility. | 10 |
| Develop reproducible open-source workflows for Digital Twin construction, validation and application. | 5 |
| Prepare manuscripts and presentations, contribute to collaborative research outputs and support open dissemination of models, software and results. The post holder must be able to lead major outputs and provide scientific guidance/mentoring to junior colleagues. | 5 |
| As agreed with line manager, any other duties commensurate with the nature of the role. |
Person Profile
Education & Qualifications
| Requirement | Importance |
|---|---|
| PhD awarded or expected within 6 months, or equivalent research experience, in Computational Biology, Bioinformatics, Genomics, Systems Biology, Data Science or a related 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 |
| Significant post-PhD experience | Essential |
Specialist Knowledge & Skills
| Requirement | Importance |
|---|---|
| Strong knowledge of bioinformatics and high-dimensional biological data analysis, particularly transcriptomics and/or single-cell data | Essential |
| Strong programming and data-analysis skills in Python and/or R | Essential |
| Practical experience of machine learning and statistical modelling, including model training, evaluation and interpretation | Essential |
| Strong understanding of pathways, molecular networks and systems-level biological analysis, and how these can be integrated with omics data | Essential |
| Ability to define and deliver an interdisciplinary computational research direction with limited supervision | Essential |
| Experience with multi-omics integration, network inference, regulatory analysis, genetics/eQTL, proteomics or phenotype data | Desirable |
| Hands-on experience with scikit-learn, PyTorch, JAX, TensorFlow or equivalent ML/deep-learning frameworks | Desirable |
| Knowledge of mechanistic modelling, Digital Twins, perturbation modelling or hybrid mechanistic-AI systems | Desirable |
Relevant Experience
| Requirement | Importance |
|---|---|
| Demonstrable experience analysing and integrating omics datasets and developing reproducible computational workflows | Essential |
| Experience applying quantitative or ML methods to biological research questions and interpreting results biologically | Essential |
| Evidence of high-quality research outputs appropriate to career stage, such as publications, software, datasets or reusable workflows | Essential |
| Evidence of scientific independence, leadership of substantial outputs/workstreams, coordination of collaborations and mentoring | Essential |
| Experience contributing to research proposals or externally funded collaborative programmes | Essential |
| Experience with mechanistic modelling or hybrid mechanistic-AI model development | 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 |
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:
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.
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Applications are invited for a Senior Computational Biologist 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 integrates patient multi-omics data with mechanistic models, GenAI and other machine-learning approaches. It is co-led by Dr Rahuman Sheriff, Ellen McDonagh (Open Targets/EMBL-EBI) and Evangelia Petsalaki (EMBL-EBI). The postholder will closely collaborate across their teams.
The role
You will provide the biological and computational bridge between rare-disease data, AI/ML and executable mechanistic models. You will analyse and integrate transcriptomic, single-cell and other multi-omics/phenotypic data, map patient data onto reusable Digital Twin models, construct and personalize hybrid Digital Twins, and lead their biological application and validation.
This is not a conventional bioinformatics-only role. You will work with the Mechanistic Modeller and AI/ML Scientist to develop hybrid modelling approaches that combine omics-derived representations, AI/ML predictions and executable models, and apply these systems to in-silico perturbation and disease-mechanism questions.
The ideal candidate
You will have strong expertise in bioinformatics/omics, excellent Python and/or R skills and practical experience with machine learning/statistical modelling. Experience with single-cell data, multi-omics integration, network biology, deep learning, mechanistic modelling or hybrid modelling would be advantageous.
The post holder must be able to evidence scientific independence, leadership of substantial computational work and the ability to coordinate interdisciplinary research with limited supervision.
Additional information
This is a full-time post for a contract of 2 years, with potential for extension depending on funding.
Salary on appointment will be within the range £47,450 to £58,000 per annum depending on qualifications and experience.
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 December 2026.
