Supervisor: Prof. Conrad Bessant
Background:
Our understanding of biology is advanced by the systematic collection of data from experiments, and formulation from these data of hypotheses that can be tested in further experiments. Although laboratory experiments and data acquisition are increasingly automated for higher throughput, experimental design and formulation of hypotheses from acquired results typically remains the responsibility of the researcher. This works well for small experiments, but the trend towards large projects using high throughput -omics methods has seen human researchers become a bottleneck as the available data and potential for new discoveries has mushroomed.
Our solution is to apply automated discovery methods to large biomolecular datasets, allowing faster exploration of the discovery space, leading to more (and perhaps more surprising) biomedical discoveries. Automated biological research was demonstrated in a seminal paper describing a “robot scientist” capable of performing laboratory experiments to develop and test hypotheses without human guidance [King et al., Nature, 2004].
Since then, repositories of biomolecular data (e.g. genomics, transcriptomics, proteomics) have become so expansive that high impact biological discoveries can be made purely from existing data. This, together with similar growth in structured databases of biological knowledge (e.g. pathways, protein structures) and rapidly developing AI methods such as large language models (e.g. GPT-4), raises the possibility of automating scientific discovery fully in silico.
The overall aim of this PhD is to devise and implement automated computational strategies to scour large experimental datasets in the context of background knowledge, identifying previously unexplained observations that can be used to automatically formulate novel hypotheses. This project is intended to make a significant step towards the ultimate goal of producing an autonomous in silico researcher, capable of making important discoveries without human input. If fully realised, this would revolutionise how biological research is conducted, massively accelerating scientific progress.
You will join a vibrant computer-based research community within the Digital Environment Research Institute, located in a dedicated recently modernised building in Whitechapel, London. You will have access to QMUL’s high performance compute facilities, and ample opportunity to develop new skills and knowledge throughout the PhD.
Queen Mary University of London is a member of the Russell group of leading research focused institutions in the UK. The successful applicant will enter a vibrant research environment, under the supervision of Prof Conrad Bessant, has over 20 years’ experience of data science, tackling research questions in analytical chemistry, biomolecular science, and qualitative healthcare studies
For details see Prof Bessant's profile pageand personal website.
Potential candidates should contact Prof Bessant by e-mail (mailto:c.bessant@qmul.ac.uk) and submit their CV and a cover letter explaining their eligibility and interest in this project.
This project is open to applicants intending to personally apply for external funding. Offers made by Queen Mary will be conditional on you being successful in applying for external funding. For example, you may directly apply to: CONACYT (Mexico) and Commonwealth Scholarships. Please only apply if you are eligible through one of these schemes.
When I have scholarships available, I will also advertise the funded studentship separately on www.findaphd.com or personal website.