Projects available

The role of Proteomics in Accelerating Translational Insights for Drug Discovery and Development 

Application Deadline - July 13th 2025. Interviews will take place online on July 21st. Candidates invited to interview will be notified in the week of July 14th.

This project will focus on advancing our understanding of the circulating proteome. You will study how proteomics can be utilised as a tool for identifying tissue dysfunction underlying diverse diseases and identifying protein signatures associated with drug intake and response, using data from population-based cohort studies and trials. 

You will learn and carry out a range of analytical approaches to identify proteins whose plasma abundance is associated with use of each of the drugs and/or drug classes of interest.  You will also develop and use machine learning algorithms to prioritise molecular markers based on a range of weighting factors, specifically technical parameters.

The project is part of the AI for Drug Discovery programme, and will benefit from being embedded within the Centre for Multi-omics in the Precision Healthcare University Research Institute (PHURI). The Centre is one of the world leaders in the genetic discovery of molecular traits, such as metabolites and proteins, which we use to identify shared genetic regulation with common, complex diseases to discover new mechanistic insights. We have a keen interest in training the next generation of scientists that can translate data into actionable insights.

Eligibility and Applying 

This project is part of the UKRI/BBSRC AI for Drug Discovery Progamme, and successful candidates will join a cohort of students working on complementary projects in the AI for Drug Discovery space.

We are looking for highly motivated individuals who are passionate about contributing to new discoveries in drug discovery bioscience through the application of the latest techniques in AI and data science. Ideal candidates will have a grounding in both a natural science and data science, e.g. through a Master's degree or work experience in a subject such as bioinformatics or computational chemistry. Alternatively, you may have, for example, a first-class degree in computer science followed by biochemistry experience, or vice versa (qualifications and evidence thereof must be obtained before October 2025). You will be confident in performing data wrangling and analysis in a language such as Python, R or C++. Effective communication skills are essential.

We particularly encourage students from groups that are currently underrepresented in postgraduate science research, including black and minority ethnic students and those from a socio-economically disadvantaged background.

The Studentship will cover UK tuition fees, UKRI stipend (currently £21,637) and a consumable allowance for a period of 4-years (pro-rata for part-time), and is open to candidates who meet the UKRI eligibility criteria. This typically means the candidate will have unrestricted access on how long they can remain in the UK (i.e. are a British National, have settled, or pre-settled status, have indefinite leave to remain etc.) and have been living in the UK for the 3 years immediately prior to studentship starting. Candidates who would be classed as International are unfortunately not eligible for this opportunity.

Visit the Apply Pages for details on how to submit an application

Supervisors:

Professor Claudia Langenberg - Director, Precision Healthcare University Research Institute, QMUL

Project Partner: GSK

Development of Generalisable Multi-Modal ML Models for Digital Pathology

Application Deadline - Applications will be reviewed on a rolling basis up until the deadline of August 3rd. The project may be awarded ahead of the August 3rd deadline and we encourage you to submit your application as soon as it is ready.

Bio-AI Health has a strong focus on development of ML based models that can help in patient screening and selection for clinical development.  Our models tend to focus on detection of key biomarkers of interest which are typically detected via some type of molecular or chemical test on a digital H&E image.  There are numerous publications which show the potential of such technology, however most of these models lack enough generalization for them to be successfully incorporated in real world practice.  Models tend to perform well when trained on specific data sets but fail when transferred to new labs or new cohorts.   

Our aim is to leverage molecular (or other) signals from these biomarkers and integrate them into our computer vision models in such a way that will allow for the model to focus on the signal of interest and lose any staining or processing bias.  This will allow us to train a generalizable model that can be effective across multiple labs / cohorts of data.  Additionally, our aim is to leverage the images plus molecular data for the development of the model, therefore vectorizing the molecular signal in such a way that we will be able to test and run the model on image data only.  Use of technology such as 10x Visium will allow for precise characterization of signal with a spatial component.  

The main objectives for the PhD project will be to:

• Model generalization across cohorts for digital pathology
• Multimodal integration of digital images and molecular signals generated from a RNAseq (or other omic data)
• Leverage spatial transcriptomics data and combine with computer vision for model development and optimization
• Help come up with innovative approaches for development of robust generalizable models on digital pathology data for personalized medicine
• Help with the integration of foundational pathology models for the fine tuning of specific tasks that allows for model generalization 

Candidates will need a strong background in deep learning, particularly computer vision, and a knowledge of omics including transcriptomics. Familiarity with histopathology images, knowledge of model generalisation techniques and experience working with multimodal AI or graph neural networks will be advantageous.  

Eligibility and Applying 

This project is part of the UKRI/BBSRC AI for Drug Discovery Progamme, and successful candidates will join a cohort of students working on complementary projects in the AI for Drug Discovery space.

We are looking for highly motivated individuals who are passionate about contributing to new discoveries in drug discovery bioscience through the application of the latest techniques in AI and data science. Ideal candidates will have a grounding in both a natural science and data science, e.g. through a Master's degree or work experience in a subject such as bioinformatics or computational chemistry. Alternatively, you may have, for example, a first-class degree in computer science followed by biochemistry experience, or vice versa (qualifications and evidence thereof must be obtained before October 2025). You will be confident in performing data wrangling and analysis in a language such as Python, R or C++. Effective communication skills are essential.

We particularly encourage students from groups that are currently underrepresented in postgraduate science research, including black and minority ethnic students and those from a socio-economically disadvantaged background.

The Studentship will cover UK tuition fees, UKRI stipend (currently £21,637) and a consumable allowance for a period of 4-years (pro-rata for part-time), and is open to candidates who meet the UKRI eligibility criteria. This typically means the candidate will have unrestricted access on how long they can remain in the UK (i.e. are a British National, have settled, or pre-settled status, have indefinite leave to remain etc.) and have been living in the UK for the 3 years immediately prior to studentship starting. Candidates who would be classed as International are unfortunately not eligible for this opportunity.

Visit the Apply Pages for further details on how to submit an application. 

Supervisors: 

Dr Vivek Singh - Lecturer in Digital Pathology, Barts Cancer Institute, QMUL

Dr Alan Jerusalmi - Chief Innovation Officer & Co-Founder, Bio-AI Health

Project Partner: Bio-AI Health