Senior Computational Biologist - AI & Rare Disease Digital Twins

Earlham Institute; Norwich; Hybrid; GBP 47,000 - 58,000; Full time

Job summary

The Earlham Institute in Norwich is seeking a Senior Computational Biologist to lead AI-enabled Digital Twins for rare diseases. The role blends bioinformatics, multi-omics analysis, and practical AI/ML with systems modelling to build reusable Digital Twin models and validate them through cross-disciplinary collaboration.

You will lead data integration, develop hybrid AI and mechanistic approaches, and drive open workflows while mentoring junior colleagues.

Qualifications

• PhD awarded or expected within 6 months in Computational Biology or related field.
• Demonstrated motivation and alignment with rare-disease Digital Twin aims.
• Evidence of independence and leadership of substantial outputs/workstreams.

Responsibilities

• Process, harmonise, analyse and integrate multi-omics data for Digital Twins.
• Lead a defined integration workstream with milestones and risk management.
• Develop hybrid mechanistic-AI modelling approaches for end-to-end Digital Twins.
• Apply Digital Twins to rare-disease questions and patient stratification.
• Prepare manuscripts and contribute to open dissemination of models and workflows.

Skills

Bioinformatics

Omics data analysis

AI/ML

Python & R

High-dimensional data

Interdisciplinary collaboration

Leadership & mentoring

Education

PhD in Computational Biology or related

Post-PhD research experience

Tools

Python

R

scikit-learn

PyTorch

JAX

TensorFlow

Genomics data analysis tools

Job description

Senior Computational Biologist - AI & Rare Disease Digital Twins

Job Title Senior Computational Biologist - AI & Rare Disease Digital Twins Post Number 1006213 Closing Date 6 Dec 2026 Grade SC5 Starting Salary 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 withother 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 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 willtake 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

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

Who We Are

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.

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.

The group develops predictive models and biological Digital Twins that combine mechanistic understanding, AI/ML