Multimodal AI for Multi-omics Data Integration

A fully funded PhD Studentship is available to work with Professor Greg Slabaugh, Professor of Computer Vision, and Director of the Digital Environment Research Institute, Queen Mary University London, and Dr Mahdi Tabassian, Associate Principal Scientist, MSD.

Awards will cover UK tuition fees, stipend at UKRI rate (currently £19,668), and a consumable allowance for 4 years (pro-rata for part-time applicants).

Applications will be reviewed on a rolling-basis up until the deadline of August 31st 2023. The studentship may be awarded prior to the August deadline and those interested in applying are encouraged to submit their application as soon as it's ready.

 Project Description

Due to advances in genomics, transcriptomics, imaging and other modalities, health datasets are becoming increasingly rich, capturing detailed information about an individual’s genotype and phenotype. Data from different modalities may provide complementary information, as well as exhibit correlated and/or causal relationships. But how to best combine multi-omics data for holistic prediction? This PhD will explore novel machine and deep learning solutions that integrate multi-omics data, with application to drug discovery science. Using neurodegenerative diseases like ALS, Alzheimer's and Parkinson’s as exemplar domains, you will research AI data modelling and fusion methods to recognise patterns across multiple modalities, addressing challenges such as handling several omics modalities, data imbalances and missing modalities/features. Expected outcomes include new general purpose AI tools widely applicable to multi-omics data along with application of these tools in the discovery of new targets and biomarkers.

The project will benefit from a number of emerging high-dimensional human datasets in neurodegenerative conditions that amalgamate transcriptomics, genetics, imaging and proteomic data and link them to disease phenotypes (e.g. AMP-AD, PPMI and Answer ALS). This presents an opportunity to explore the state-of-the-art multimodal integration approaches to define molecular underpinning on these conditions to define causal biology of the disease and ultimately leading to new ways of improving health in these debilitating conditions. .

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.

 Eligibility and applying 

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 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 18/09/2023). You will be confident in performing data wrangling and analysis in a language such as Python, R or C++. ​Effective communication​ skills are essential. 

Candidates must meet the UKRI requirements to be eligible for the award. Typically this means candidates 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. This award is unfortunately not available to students who would be classed as international.

Applications will be reviewed on a rolling-basis up until the deadline of August 31st 2023. The studentship may be awarded prior to the August deadline and those interested in applying are encouraged to submit their application as soon as it's ready. 

The projects start in September 2023. For more details, including eligibility and how to apply, see https://www.qmul.ac.uk/deri/ukri-aidd-doctoral-training-programme/apply/