Investigating cellular mechanisms underlying lung fibrosis using omics approaches

About the Project

Lung fibrosis kills thousands annually with no curative treatment. This project decodes the cellular and molecular mechanisms driving fibrosis initiation and progression, combining human models, spatial multi-omics, and AI/ML-based computational analysis of patient lung tissue. You will generate and interpret cutting-edge multimodal data to identify regulatory drivers and biomarkers.

Lung Fibrosis (IPF: Idiopathic Pulmonary Fibrosis, ILD: Interstitial Lung Diseases) are devastating chronic conditions characterised by lung scarring and irreversible respiratory decline. This project investigates the cellular and molecular mechanisms that maintain tissue health and failure in fibrosis. Working across experimental and computational approaches, you would use human primary culture models alongside next-generation sequencing assays to generate datasets (single-cell, spatial). The datasets will be integrated with existing multimodal spatial atlases to study mechanisms underlying fibrosis.

Computationally, the project will develop and apply AI/ML-based integration frameworks and gene regulatory network inference tools to identify master transcriptional regulators and candidate biomarkers across fibrotic progression. These markers, testable hypotheses will be validated in experimental models.

The project carries strong multidisciplinary co-supervision spanning basic research, clinical science, and computational biology, with active collaborations across Southampton Faculties (SoBS, Medicine). This environment is equally suited to ambitious experimentalists seeking to develop quantitative computational skills, computational candidates with strength, skills in machine learning or AI applied to biomedical and omics data, and candidates working at the wet-dry interface in lung biology (IPF/ILD research).

Additional Training in relevant quantitative analysis (computational tools, statistics) and experimental biology would be provided.

Entry Requirements

• Undergraduate degree (UK 2:1 honours or international equivalent) in Biological Sciences, Biochemistry, Biomedical Sciences, Bioinformatics, Computer Science or a closely related discipline
• Strong academic background in at least one of: molecular and cell biology, respiratory/pulmonary biology, single-cell/spatial genomics, or computational/data science applied to biomedical questions
• Demonstrable enthusiasm for rigorous, interdisciplinary research at the interface of experimental biology and quantitative analysis

Desirable (Experimental): Hands-on experience with mammalian cell culture, including primary cells or organoid systems; Practical experience with molecular biology assays and/or NGS library preparation workflows; Prior research exposure to lung biology, fibrosis, or related respiratory disease contexts

Desirable (Computational): Programming proficiency in Python or R; Prior analysis experience/Exposure to omics datasets (single-cell transcriptomics, spatial omics, or bulk RNA-seq data analysis); Experience in machine learning, deep learning, or AI methods applied to biological data, including IPF, ILD, or related lung disease contexts