Postdoctoral Research Scientist - Generative AI & Digital Twins
Posted 2 days 23 hours ago by NBI
Permanent
Full Time
Research Jobs
Norfolk, Norwich, United Kingdom, NR1 1
Job Description
Postdoctoral Research Scientist - Generative AI & Digital Twins
Job Title Postdoctoral Research Scientist - Generative AI & Digital Twins Post Number Closing Date 6 Dec 2026 Grade SC6 Starting Salary Salary: £39,000-£46,500
Hours per week 37 Project Title Project TitleDevelopment 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 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 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 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 musttake 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. Essential 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
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
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 . click apply for full job details
Job Title Postdoctoral Research Scientist - Generative AI & Digital Twins Post Number Closing Date 6 Dec 2026 Grade SC6 Starting Salary Salary: £39,000-£46,500
Hours per week 37 Project Title Project TitleDevelopment 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 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 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 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 musttake 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. Essential 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
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
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 . click apply for full job details