Location: South Kensington and White City
The project: Bioproduction in mammalian cell lines is a rapidly expanding industry of significant importance for the production of biotherapeutics and vaccines. A key challenge is to develop robust, predictable, and sustainable genetic expression. The design of enhanced mammalian promoters and genetic circuits is therefore a key strategic industrial target.
In this project, you will develop and implement Machine Learning (ML) methods for graph learning using Graph Neural Networks (GNNs) applied to large-scale transcriptomic datasets with the objective to optimise engineered promoters in mammalian cells. The project will focus on (1) developing GNN learning methods to predict eukaryotic gene expression in context by leveraging ML on mammalian promoter sequences obtained from large-scale publicly available databases (e.g. the EPD [10.1093/nar/gkw1069], the DEE2 uniform transcriptomic database [10.1093/gigascience/giz022], and the broader SRA database [10.1093/nar/gkq1019]); and (2) using the trained GNNs to propose new mammalian promoter sequences optimised for bioproduction using insights from the GNN model. Experimental demonstration in mammalian cell lines will be performed by project collaborators using automated DNA assembly and analytics capabilities available at the London Biofoundry (https://www.londonbiofoundry.org).
Key duties and responsibilities
The ideal candidate will have PhD-level expertise in in machine learning, artificial intelligence, data science, applied maths, computer science, bioengineering, or a closely related area. Proficiency in deep learning, graph learning, graphical neural networks applied to analysis and design of biochemical or biological systems is highly desirable.
You will have the opportunity to present the results at international conferences and to write research and review articles in the appropriate journals. You will help in the supervision of PhD, Master and Undergraduate students, collaborating with their supervisors and teams.
Essential requirements
The ideal candidate will be highly motivated and excited about machine learning and artificial intelligence applied to the design of biological systems. He/She will have expertise on some of the following fields: machine learning/artificial intelligence for biosystems analysis and design, data-based analysis and design of biosystems, computer science, data science, systems biology, synthetic biology, engineering biology.
The position requires a strong commitment to high-quality research, excellent communication skills, and the ability to work diligently and cooperatively with others. Successful applicants will be able to work independently, and display the intellectual curiosity required to effectively lead multidisciplinary projects.
Further Information
This position is full time, fixed term for 22 months.
Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £36,694 – £39,888 per annum.
Informal enquires can be made directly to [email protected].
Any queries regarding the application process should be directed to Yusra Vallimohamed at [email protected]
For technical issues when applying online please email [email protected]
Committed to equality and valuing diversity, we are an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employer and work in partnership with GIRES to promote respect for trans people.
Closing date: 14/06/2022
To apply, visit www.imperial.ac.uk/jobs and search by the job reference ENG02159.