The Edinburgh Genome foundry are looking for two post-doctoral researchers to work with the School of Engineering at the University of Edinburgh.
Research Associate in Mammalian Synthetic Biology
Closing Date: 18-Jan-2019
Vacancy Ref: #046305
Contact Person: Dr Filippo Menolascina (Filippo.Menolascina@ed.ac.uk)
A 3 year, fixed term postdoctoral appointment is available within the School of Engineering at the University of Edinburgh to work on the automatic (re)design of synthetic promoters, primarily for mammalian cells, focusing on the control Chimeric Antigen Receptors expression.
As part of this project, the successful candidate will develop a microfluidics-based platform to perform high-throughput cell screening and will liaise with the Edinburgh Genome Foundry to build, and automatically model, large libraries of synthetic inducible promoters.
They will combine machine learning and computational optimisation to predict promoter strength, leakiness and automatically optimise promoter design to meet set specifications (e.g. maximise fold induction, minimise response time). They will also build a promoter to maximise sensitivity/specificity of transgene expression.
The ideal candidate should have a PhD with a background in Engineering or Computer Science and previous experience with techniques/protocols in Cell Biology and Microscopy. Experience with microfluidic device fabrication is desirable.
Research Associate in Microbial Synthetic Biology
Closing Date: 18-Jan-2019
Vacancy Ref: #046306
Contact Person: Dr Filippo Menolascina (Filippo.Menolascina@ed.ac.uk)
A 2 year, fixed term postdoctoral appointment is available within the School of Engineering at the University of Edinburgh to work on the automatic engineering of synthetic microbial promoters.
As part of this project, the successful candidate will develop a microfluidics-based platform to perform high-throughput cell screening and will liaise with the Edinburgh Genome Foundry to build, and automatically model, large libraries of synthetic inducible promoters.
They will combine machine learning and computational optimisation to predict promoter strength, leakiness and automatically optimise promoter design to meet set specifications (e.g. maximise fold induction, minimise response time). They will also build a promoter to maximise sensitivity/specificity of transgene expression.
The ideal candidate should have a PhD with a background in Engineering or Computer Science and previous experience with techniques/protocols in Cell Biology and Microscopy. Experience with microfluidic device fabrication is desirable.