Post Details
- Job Title: Postdoctoral Research Scientist - Mechanistic Modelling for Rare Disease Digital Twins
- Post Number: 1006212
- Closing Date: 6 Dec 2026
- Grade: SC6
- Starting Salary: £39,000 - £46,500
- Hours per week: 37
- Project Title: Development of Digital Twins for modelling rare diseases and exploring potential applications
- Months Duration: 24
Main Purpose of the Job
The post holder will conduct primary research in the Sheriff Group to develop executable mechanistic models and reusable Digital Twins for rare diseases within the Chan Zukerberg Initiative-funded Digital Twin program.
The role will focus on adapting curated models and pathways from BioModels, Reactome and the literature; constructing de novo mechanistic models from disease- and phenotype-specific networks; parameterising and validating models using biological and multi-omics data; and integrating mechanistic models with AI/ML components and patient-derived data to create hybrid Digital Twins. Differential-equation and dynamical modelling will be central, while there will also be opportunities to use stochastic, logical, constraint-based, agent-based and other modelling approaches where scientifically appropriate.
The post holder will contribute to new Digital Twin methodology, in-silico perturbation, model personalisation and hybrid mechanistic-AI modelling. The postholder 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 work across this interdisciplinary collaboration and with the wider rare-disease, mechanistic and AI-ML modelling communities.
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
| Activity | Percentage |
|---|---|
| Develop, adapt, parameterise and validate executable mechanistic models of disease-relevant molecular and cellular processes, with particular emphasis on ODE/dynamical models; construct de novo models from context-specific molecular networks and reuse appropriate BioModels, Reactome and published models. | 35 |
| Develop reusable Digital Twin models and methods for model personalisation, calibration and integration of patient-derived multi-omics data. | 20 |
| Work with AI/ML and computational biology researchers to develop hybrid mechanistic-AI approaches, including interfaces between mechanistic simulations and learned components, AI-assisted state/parameter inference, surrogate or learned components, and integration of predicted perturbation responses. | 20 |
| Perform in-silico perturbation experiments, parameter estimation, sensitivity/uncertainty analyses and model validation; compare healthy and disease states and generate biologically testable predictions. | 10 |
| Contribute to reusable and reproducible open-source modelling software and workflows, including development/extension of BioModels-S2D, SimBio and appropriate community standards such as SBML/SED-ML. | 10 |
| Prepare manuscripts and presentations, contribute to collaborative research outputs and support open dissemination of models, software and results. | 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 in Systems Biology, Mathematical/Computational Biology, Applied Mathematics, Physics, Bioengineering, Computer Science 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 mechanistic/mathematical modelling of biological systems, with practical expertise in ODE or dynamical modelling. | Essential |
| Strong scientific programming skills in Python and/or another relevant language such as Julia, MATLAB or R. | Essential |
| Knowledge of numerical simulation and model calibration, with experience in one or more of parameter estimation, sensitivity analysis or uncertainty analysis. | Essential |
| Ability to translate biological hypotheses, pathways or molecular networks into executable computational models. | Essential |
| Knowledge of stochastic, logical/Boolean, constraint-based, agent-based or multiscale modelling. | Desirable |
| Ability to run bioinformatic workflows to analyse omics datasets such as transcriptomics and genomics data. | Desirable |
| Knowledge of machine learning, scientific machine learning or hybrid mechanistic-AI modelling. | Desirable |
| Knowledge of BioModels, Reactome, SBML, SED-ML or FAIR/reproducible modelling. | Desirable |
Relevant Experience
| Requirement | Importance |
|---|---|
| Demonstrable experience developing, fitting and analysing mechanistic models of biological systems. | Essential |
| Experience interpreting model behaviour and comparing model predictions with experimental or observational data. | Essential |
| Experience with biological omics/single-cell data or integration of data with mechanistic models. | Desirable |
| Evidence of high-quality research outputs appropriate to career stage, such as publications, modelling software or reusable models. | 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:
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 Postdoctoral Research Scientist (SC6) to join the Sheriff Group that develops AI-integrated Digital Twins in the Research Faculty of the Earlham Institute, Norwich, UK.
Background
The post is part of a Chan Zuckerberg Initiative-funded collaboration developing open-source Digital Twins for rare diseases. Rare-disease Digital Twins in this programme are purpose-built hybrid computational models that combine mechanistic models, AI/ML and patient-derived multi-omics data to simulate disease-relevant biological processes and perturbations. The program is co-lead by Dr Rahuman Sheriff, Ellen McDonagh (Open Targets/EMBL-EBI) and Evangelia Petsalaki (EMBL-EBI), involving close collaboration across their teams. The postholder will closely collaborate across their teams.
The role
You will lead the mechanistic systems biology modelling component of project, developing executable models and reusable Digital Twin models by adapting relevant models from BioModels, Reactome and the literature and constructing de novo models from disease- and phenotype-specific networks. ODE/dynamical modelling will form the core of the role, with opportunities to use other mechanistic frameworks including stochastic, logical, constraint-based and agent-based modelling where appropriate.
A major focus will be integration with AI/ML. You will work closely with computational biology and AI/ML researchers to develop hybrid mechanistic-AI methods, personalise models using multi-omics data, perform in-silico perturbations, quantify uncertainty and validate model predictions. You will also contribute to reusable open-source Digital Twin software and reproducible model dissemination.
Ideal candidate
You will have a PhD in a relevant quantitative discipline, strong experience in mechanistic modelling of biological systems, and strong scientific programming skills. Experience with dynamical models is essential; experience with omics, machine learning, BioModels/community standards or additional modelling paradigms would be advantageous.
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.
For further information and details of how to apply, please visit our website or contact the Human Resources team on 01603 450814 quoting reference 1006212.
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.
