Postdoc position at Imperial College London on “Graph learning methods for engineering mammalian promoters in bioproduction”
Jhony Heriberto Giraldo Zuluaga
Dear all,
Thank you very much for diffusing the following postdoc position.
As part of a large
consortium focused on AI and Engineering Biology (AI-4-EB), Prof. Guy-Bart Stan has a ~2-year postdoctoral position in the Department of
Bioengineering at Imperial College London dedicated to the use and
development of artificial intelligence/machine learning methods
for synthetic biology/engineering biology (see details below).
I would be very
grateful if you could propagate via your networks and/or send this
information directly to suitable postdoc candidates.
Please feel free to contact me directly or to contact Prof. Guy-Bart Stan for further details.
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Imperial College London: Open ~2-year 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, we will design and implement Machine Learning (ML) methods for graph learning using Graph Neural Networks (GNNs) applied to large-scale transcriptomic datasets so as to facilitate forward engineering and optimisation of engineered promoters in mammalian cells. We will in particular focus on (1) developing GNN learning methods to predict eukaryotic gene expression in context by leveraging ML on knowledge graphs of promoter sequences from large-scale publicly available databases, such as 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. Graph learning integrates diverse and heterogeneous data to uncover nuanced statistical relationships in structured domains, allowing us to form a more complete and general picture of promoter behaviour than when considering the genetic sequence alone.
As deep learning and graph-learning networks play major roles in the project, you ideally will have a PhD in machine learning, artificial intelligence, applied maths, data science, computer science, bioengineering, or a closely related area.
Supervisors: Profs. Guy-Bart Stan, Karen Polizzi, and Francesca Ceroni at Imperial College London.
Official advert, job descriptions and link to the application website (deadline 14 June 2022): https://www.imperial.ac.uk/jobs/description/ENG02159/postdoctoral-research-associateassistant-graph-learning-methods-engineering-mammalian-promoters