Engineering AI and deep learning approaches for digital pathology and bioimaging

An exciting opportunity to join a dynamic and multidisciplinary group at the School of Cancer and Pharmaceutical Sciences. You will develop new computational pathology approaches to improve cancer patient treatment and diagnosis. This interdisciplinary project is suited for graduates with backgrounds in Computer Sciences, Biomedical Engineering, Computer Vision, Bioinformatics, or Mathematics. Candidates with interest in lab-based projects would also be considered.

Project description

Breast cancer poses a significant challenge in the UK, especially for patients diagnosed at an advanced stage, leading to uncertainties in the best treatment strategies. The project aims to address this unmet need by developing personalized treatment strategies. Histopathology, the standard for cancer patient diagnosis, involves evaluating cellular and tissue changes. While artificial intelligence (AI) and machine learning have shown promise in analyzing large-scale cancer images, translating these models into clinical practice remains challenging. This project focuses on leveraging AI and deep neural networks to predict patient response to treatment by systematically scoring tumor composition and topology. The goal is to stratify patients for tailored treatments and explore alternative pathways, including participation in clinical trials.

As a PhD candidate:

1-   Develop a robust AI framework for large-scale imaging and genomic data, emphasizing explainability and trustworthiness.

2- Investigate and mitigate model biases to ensure fairness.

3- Integrate various deep learning models by creating knowledge graphs.

Why Apply?

Through this project, you will join a highly collaborative and enthusiastic team to innovate solutions that transform cancer patient lives. You will develop a diverse skill set, including cutting-edge knowledge in computer vision, deep learning, and computational techniques and their application in biomedicine. You will collaborate closely with clinicians, designing bespoke visualization methods and ensuring the practicality of your approaches in clinical settings. Check the group website for further details at www.hebasailem.com.

Candidates must possess a first or upper-second-class degree in a relevant scientific field.

To apply, email your CV, contact details of two academic referees, and a personal statement detailing your suitability for the project to [Email Address Removed].