Do you want to help us improve human health and understand life on Earth? Make your mark by shaping the future to enable or deliver life‑changing science to solve some of humanity’s greatest challenges.
We are hiring a Postdoctoral Fellow / Senior Postdoctoral Fellow to join our interdisciplinary team at the forefront of computational biology and AI for a 3 year fixed term contract. You will contribute to (lead - Senior Postdoctoral Fellow) transformative projects that integrate single‑cell genomics, spatial transcriptomics, and generative AI to build next‑generation models for understanding tissue biology and cellular dynamics across organs such as the pancreas, kidney, skin, and liver.
We welcome applicants from diverse technical and scientific backgrounds — from those interested in fundamental questions in biology and medicine, to those focused on ML / AI method development. We are particularly excited to work with individuals who are passionate about biology, foundation model development, modelling cellular perturbation responses, predicting patient behaviours, and analysing multi‑modal biological data.
Available Research Focus Areas : 1. Spatial & Multi‑omics Atlas Construction
Build large‑scale spatial and single‑cell atlases across diseased tissues (pancreas, kidney, skin, liver) using spatial transcriptomics, scRNA‑seq, and multiome data in collaboration with leading Sanger groups.
2. Generative AI for Cell Fate & Perturbations
Develop diffusion, flow‑matching, and transformer‑based generative models to predict cell fate, tissue remodelling, and drug or perturbation responses.
3. Foundational Models for Single‑Cell Biology
Train large, generalisable deep models across public and internal datasets to support the Human Cell Atlas and broad Sanger research programmes.
4. Open Targets Translational AI Projects
Apply foundational and multi‑omics models to real‑world challenges in drug discovery, target identification, and target safety in collaboration with major pharma partners.
5. Agentic AI for Scientific Reasoning & Experiment Design (new)
Develop AI agents capable of hypothesis generation, experiment planning, and multi‑step scientific workflows using reinforcement learning and tool‑use models.
6. Core Machine Learning Research
Advance fundamental ML methods—including advanced generative modelling, scalable training algorithms, representation learning, and uncertainty modelling—tailored for biological data.
7. Multimodal Learning (Imaging + Genomics + Clinical Data)
Create models that integrate histopathology imaging, spatial proteomics, single‑cell genomics, and patient‑level clinical data to learn unified biological and clinical representations.
8. Leap Project – We are interested in developing large‑scale AI models to stratify patients using diverse multi‑omics data, with a strong commitment to equity and inclusion, particularly in women’s health. This work is being undertaken in collaboration with the Sanger Institute.
The Open Targets (OT) research programme generates and analyses data to connect targets to diseases, assess the strength of this evidence, and help identify and prioritise targets for drug discovery. This includes evidence that causally links targets and diseases, as well as foundational data that helps us understand biological processes and disease progression more deeply.
About Us : You will join the Lotfollahi Group , an interdisciplinary team of ML researchers, computational biologists, clinicians and experimentalists. Our mission is to develop data‑driven and biologically grounded AI tools for decoding complex cellular systems. We collaborate closely with the Human Cell Atlas, Sanger's single‑cell programmes, and international leaders in the field.
Key Publications and References :
- Akbar Nejat et al., Mapping and reprogramming human tissue microenvironments with MintFlow (bioRxiv, 2025)
- Birk et al., Quantitative characterization of cell niches in spatially resolved omics data, Nature Genetics (2025)
- Jeong et al., SIGMMA : Hierarchical Graph‑Based Multi‑Scale Multi‑modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome (arXiv, 2025)
- Sanian et al., 3D‑Guided Scalable Flow Matching for Generating Volumetric Tissue Spatial Transcriptomics from Serial Histology (arXiv, 2025)
What We Offer :
- Access to unique in‑house datasets and world‑class computational infrastructure
- Opportunities to co‑lead publications and present at ML and genomics conferences
- Collaborative and inclusive environment with strong mentorship culture
- Access to internal training, workshops, and career development resources through our Postdoctoral Fellow Programme.
About You : We are looking for enthusiastic researchers with a strong computational or quantitative background.
Essential Skills :
- PhD in relevant subject area, or on track to be awarded your PhD within 6 months of starting the role
- Proven ability to deliver research projects
- A track record of demonstrating research excellence and expertise in your area of research
- Experience in advanced statistical techniques, machine learning and modern deep learning techniques
- Experience with single‑cell omics, spatial transcriptomics, or large‑scale biological data integration
- Knowledge of Python, including core‑data science libraries such as Sci‑Kit‑Learn, SciPy, TensorFlow and PyTorch
- Knowledge of software development good practices and collaboration tools, including git‑based version control, python package management and code
- Proven ability to develop and maintain effective working relationships with wide range of persons of differing level, abilities and knowledge
- Foster an inclusive culture where all can thrive and diversity is celebrated
- Team player with the ability to work with others in a collegiate and collaborative environment
- Ability to effectively communicate ideas and results and present orally to groups
- Commitment to personal development and updating of knowledge and skills
- Ability to prioritise, multi‑task and work independently
- Detailed orientated, strong organizational and problem‑solving skills
Additional Skills for Senior Postdoctoral Fellow :
- Strong knowledge of Python, including core data science libraries such as Scikit‑Learn, SciPy, Tensorflow and PyTorch
- Proven experience using advanced statistical statistical techniques, machine learning and modern deep learning techniques
- Proven ability to work independently and deliver research projectsRelevant solid publication record in either machine learning or application of machine learning in biology
- Strong influencing skills to engage with internal and external stakeholders
- Critical and analytic thinking around problems
- Demonstrable good time management and project management skills
Other Information : For further details, please see role profiles for Postdoctoral Fellow and for Senior Postdoctoral Fellow.
Salary per annum (dependent upon skills and experience) :
Postdoctoral Fellow - £38,000-£49,156
Senior Postdoctoral Fellow - £43,650 - £49,156
