PhD Studentship: Machine Learning for Spatial Transcriptomics Analysis in Human Cancers: Tackling Limited Data Challenges

This 3.5 year PhD is fully funded. Tuition fees will be paid and you will receive a tax free stipend set at the UKRI rate (£19,237 for 2024/25). This funding is for home students only and EU students with settled status.

This project addresses the pressing challenge of data scarcity in spatial transcriptomics (ST), crucial for advancing cancer research. ST data, revealing spatial and gene expression information in cancer cells, holds transformative potential for understanding cancer biology. However, its advancement is hindered by data scarcity, which restricts the application of advanced machine-learning techniques in ST studies. Our approach introduces two innovations: developing sparse Bayesian learning algorithms for efficient small dataset analysis and designing a simulator for generating synthetic data. Incorporating human-in-the-loop ensures model interpretability. By addressing data scarcity, this initiative aims to promote significant impacts on both research and clinical oncology.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

We strongly recommend that you contact the supervisor for this project before you apply. Please send an email to hongpeng.zhou@manchester.ac.uk.

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