Postdoctoral Research Associate/Assistant: Graph learning methods for engineering mammalian promoters in bioproduction
Department of Bioengineering
BBSRC-funded postdoc position on “Graph learning methods for engineering mammalian promoters in bioproduction” (to be filled as soon as possible)
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 ).
As deep learning and graph-learning networks play major roles in this project, you will ideally have a PhD in machine learning, artificial intelligence, data science, applied maths, computer science, computational systems biology or synthetic biology, bioengineering, or a closely related area.
Supervisors: Profs. Guy-Bart Stan, Karen Polizzi, and Francesca Ceroni at Imperial College London (if interested, please apply online with your CV and expression of interest, main point of contact: email@example.com ).
Duties and responsibilities
The group and the working environment: Prof Guy-Bart Stan ( http://www.imperial.ac.uk/people/g.stan ) is leading the Control Engineering Synthetic Biology Group ( https://gstan.bg-research.cc.ic.ac.uk/group/index.shtml ) at Imperial College London, working at the interface between synthetic biology, machine learning and control engineering with applications in biotechnology, biomedicine and agritech. Research in the Stan Group focuses on developing engineering methods and tools to facilitate the analysis, design and control of synthetic gene constructs so as to endow cells with novel functionalities that can be used to tackle some of the most pressing grand challenges of this Century.
The project is part of a larger UK Research Consortium led by Imperial College and focused on developing artificial intelligence for engineering biology (AI-4-EB Consortium), i.e. developing AI/ML design methods and predictive modelling tools to direct and accelerate the wet lab engineering of biological systems.
The project will be supervised by Profs Guy-Bart Stan ( http://www.bg.ic.ac.uk/research/g.stan/ ), Karen Polizzi ( https://www.imperial.ac.uk/people/k.polizzi ), and Francesca Ceroni ( https://www.imperial.ac.uk/people/f.ceroni ) at Imperial College London.
During the project, you will have the opportunity to collaborate with other members of the Stan Group ( https://gstan.bg-research.cc.ic.ac.uk/group/people.shtml ), Polizzi Group ( https://sites.google.com/site/polizzilab/the-team ), Ceroni Group and of the Imperial College Centre of Excellence in Synthetic Biology ( http://www.imperial.ac.uk/synthetic-biology/centre/ ) as well participate in the supervision of PhD, Masters, and Undergraduate students. In addition, for the proposed role, you will work closely with other Research Associates, in particular theoreticians and experimentalists working in the Centre of Excellence in Synthetic Biology.
The Stan Group is part of the Department of Bioengineering ( http://www.imperial.ac.uk/bioengineering ) and belongs to the world-leading Imperial College Centre of Excellence in Synthetic Biology ( http://www.imperial.ac.uk/synthetic-biology/centre/ ) of which Prof Stan is the Co-Director. The Polizzi and Ceroni Groups are part of the Department of Chemical Engineering ( https://www.imperial.ac.uk/chemical-engineering ) at Imperial College London.
You will also benefit from training and support from the Postdoc and Fellows Development Centre ( https://www.imperial.ac.uk/postdoc-fellows-development-centre ) and its multi-award-winning career development programmes.
You will work in the group of Prof Guy-Bart Stan and closely with other research staff, students, and collaborators on the project. In addition, you will have the opportunity to collaborate with other well-known scientists at Imperial College, as well as other international academics and companies.
You will be responsible for the development of the Machine Learning methods and data-based pipeline for the analysis and design of mammalian promoter sequences optimised for bioproduction.
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.
You will work in a friendly, collaborative and multidisciplinary environment with high training and career development opportunities.
The ideal candidate will be highly motivated, dynamic, 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.
Closing date: 18 February 2022
Start date: 1 April 2022 (or asap)
Supervisors:Professors Guy-Bart Stan, Karen Polizzi and Francesca Ceroni at Imperial College London.
This is a fixed term position for 22 months. Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £36,694 – £39,888 per annum.
Informal queries relating to the position should be directed to Dr Guy-Bart Stan at firstname.lastname@example.org .
Our preferred method of application is via our website. Please click “APPLY NOW” to go through to the online application form.
Should you have any queries regarding the application process please contact: Yusra Vallimohamed: email@example.com
The Department of Bioengineering leads the bioengineering agenda both nationally and internationally, with its staff coming from diverse academic disciplines, including all main branches of engineering, physical sciences, life sciences and medicine, to create a rich collaborative environment. More information about staff benefits, including generous annual leave entitlements and excellent professional development opportunities, can be found here: “skill” http://www.imperial.ac.uk/job-applicants/staff-benefits/
Please note that job descriptions cannot be exhaustive, and the post-holder may be required to undertake other duties, which are broadly in line with the above key responsibilities.
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