(Senior) Postdoctoral Research Scientist - Generative AI and Closed-loop Discovery

(Senior) Postdoctoral Research Scientist - Generative AI and Closed-loop Discovery

Job Title (Senior) Postdoctoral Research Scientist - Generative AI and Closed-loop Discovery Post Number Closing Date 20 Aug 2026 Grade SC6/SC5 Starting Salary Salary: £39,000-£52,560

Hours per week 37 Project Title Generative Digital Biology: AI-Guided Biological Design and Closed-Loop Scientific Discovery Expected/Ideal Start Date 07 Sep 2026 Months Duration 36

Job Description Main Purpose of the Job The post holder will conduct primary research in the AI for Biology Group to develop generative, causal and decision-making AI methods for biological design and closed-loop discovery. The role will focus on Bayesian decision-making, experimental design and original AI algorithms for modeling high-dimensional biological design spaces with interpretable and uncertainty-aware representations. Using these representations, the post holder will develop methods to generate testable hypotheses, propose candidate biological designs, prioritise experiments, reason over biological constraints and learn from experimental feedback. This post will form the design and discovery engine of the Generative Digital Biology programme. Working alongside established foundation model research in the group, the successful candidate will develop methods that connect biological representation learning with generative modelling, reinforcement learning, Bayesian experimental design, active learning, uncertainty quantification, causal modelling, multi-objective optimisation and combinatorial optimisation. The successful candidate will join at a rare moment: early enough to help shape a new AI for Biology programme at EI, but with strong algorithmic foundations, prior publications, existing collaborations and a clear research trajectory already in place. The ambition is to move beyond models that only predict biological properties, towards AI systems that can propose, refine and prioritise biological designs and experiments. Application areas may include sequence and RNA design, regulatory elements, perturbation design, synthetic constructs, cellular states, genotype-to-phenotype landscapes, engineering biology, plant systems, human health and therapeutic discovery collaborations where appropriate. This will be a highly collaborative role embedded across EI. The post holder will work with EI colleagues and platforms to connect AI-designed hypotheses and candidates with biological data, experimental design and validation routes, including potential collaborations with Earlham Biofoundry and engineering biology colleagues on AI-guided design-build-test-learn cycles; with Cellular Genomics and Single-cell and Spatial Analysis on perturbation, cell-state and single-cell/spatial omics use cases; and with BioFAIR, ELIXIR-UK, Open and FAIR Data and Research e-Infrastructure colleagues on AI-ready design datasets, benchmarks, provenance and reproducible workflows. The post holder will be expected to lead high-quality research outputs, publish in leading AI, machine learning, computational biology and life science venues, contribute to open and reproducible algorithms, software and benchmarks, and support future competitive grant applications to UKRI, EPSRC, BBSRC, Wellcome, ERC and related funders.

Key Relationships INTERNAL: Reporting to Professor Ke Li, the post holder will work closely with the AI for Biology Group and collaborate across EI's research programmes, National Bioscience Research Infrastructures and technology platforms. Key internal relationships are expected to include BioFAIR, ELIXIR-UK, and Open and FAIR Data colleagues; Research e-Infrastructure; the Cellular Genomics programme; the Single-cell and Spatial Analysis platform; Earlham Biofoundry and engineering biology colleagues; Transformative Genomics; High-Performance Sequencing; and relevant EI scientific groups working on plants, microbes, biodiversity, health, genomics and data-intensive bioscience. The role is intended to help make the AI for Biology Group a collaborative AI engine for EI, supporting AI-ready design datasets, generative-design benchmarks, provenance-aware experimental records, model-guided experimental design and closed-loop discovery workflows across the Institute. Internal and external collaborations may occur as described.

EXTERNAL: The post holder will interact with UK and international collaborators in AI, machine learning, computational biology, genomics, single-cell and spatial biology, engineering biology, plant science, human health and therapeutic discovery. External collaborations may include academic, clinical, public-sector, infrastructure and industry partners where appropriate.

Main Activities & Responsibilities Percentage Develop original generative, causal AI and optimisation methods for biological design, hypothesis generation, perturbation prioritisation and experimental discovery. For appointment at SC5, take intellectual and operational leadership of a defined generative or closed-loop discovery workstream, set scientific priorities and milestones, manage technical risks, and deliver the work with limited supervision (essential for SC5) 25 Develop theoretical foundations and practical algorithms using approaches such as diffusion models, flow models, autoregressive models, energy-based models, reinforcement learning, Bayesian optimisation, active learning, causal learning, multi-objective optimisation or combinatorial optimisation. 20 Develop closed-loop experimental design methods that combine uncertainty quantification, multi-fidelity modelling, safe exploration, biological constraints and lab-in-the-loop feedback. 15 Integrate foundation models, biological priors, mechanistic knowledge, causal representations, genotype-to-phenotype landscapes or fitness landscapes to guide the design of DNA/RNA/protein sequences and functions, regulatory elements, perturbations, synthetic constructs or cellular states. 15 Collaborate with Earlham Biofoundry, engineering biology, Cellular Genomics, Single-cell and Spatial Analysis, BioFAIR, ELIXIR-UK and other EI colleagues to identify biological use cases, define AI-ready design datasets and prioritise candidates for experimental validation. For appointment at SC5, coordinate the relevant interdisciplinary collaboration and take responsibility for translating methods into a coherent experimental-validation plan (essential for SC5) 10 Develop benchmark tasks, ablation studies, robustness/generalisation analyses, constraint-satisfaction evaluations, uncertainty estimates and biological validity checks for generative and design algorithms. 5 Prepare manuscripts and conference papers for leading AI, machine learning, computational biology and life science venues; present findings internally, nationally and internationally. For appointment at SC5, lead the preparation and submission of major research outputs and represent the work in relevant external forums (essential for SC5) 5 Contribute to research proposals, grant applications, open-source software, reproducible workflows, benchmark documentation and good research practice, including responsible data handling and reproducibility. For appointment at SC5, make substantive contributions to grant development and provide scientific or technical guidance to junior researchers or students (essential for SC5) 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 Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, Mathematics, Statistics, Physics, Engineering or a related quantitative discipline Essential

Specialist Knowledge & Skills Requirement Importance Strong knowledge in one or more of modern machine learning, generative modelling, reinforcement learning, Bayesian optimisation, active learning, uncertainty quantification, multi-objective optimisation or combinatorial optimisation Essential Excellent programming skills in Python and practical experience with PyTorch, JAX, TensorFlow, BoTorch, GPyTorch, Pyro, NumPy/SciPy or equivalent AI/scientific computing frameworks Essential Experience designing, implementing and evaluating original AI algorithms for design, optimisation, decision-making, experimental design, generative modelling or AI-for-science problems Essential Understanding of biological data or design problems, such as high-dimensional combinatorial or mixed-integer search spaces, DNA/RNA/protein sequences, genomics, transcriptomics, single-cell/spatial data, perturbation data, synthetic biology, engineering biology, molecular design or drug discovery Desirable Demonstrable experience in closed-loop or active experimental design, or an equivalent sequential decision-making setting with real-world feedback (essential for SC5) Desirable Experience with biological foundation models, sequence design, inverse design, structure-aware design, perturbation modelling, genotype-to-phenotype modelling or fitness landscapes Desirable A strong track record of independent or semi-independent research in machine learning, AI, computational biology, bioinformatics, or a closely related field (essential for SC5) Desirable Ability to develop and deliver a research direction with limited supervision, including project planning, collaboration and communication with interdisciplinary partners (essential for SC5) Desirable

Requirement Importance Experience developing original AI methods rather than only applying existing tools to biological datasets Essential Experience working on generative design, reinforcement learning, Bayesian optimisation, causal discovery, active learning, uncertainty quantification . click apply for full job details