PostDoctoral Research Associate position at Imperial College London for the project "Data-based optimal control of synthetic biology gene circuits" in the Control Engineering Synthetic Biology group

The Control Engineering Synthetic Biology group (CESB) located within the Centre for Synthetic Biology and Innovation and the Department of Bioengineering at Imperial College London (U.K.) is currently looking to hire an experienced and motivated PostDoctoral Research Associate to work on the U.K. EPSRC funded research project EP/J014214/1: "Data-based optimal control of synthetic biology gene circuits". We are seeking highly motivated, bright researchers and our group is a dynamic, highly productive and stimulating environment, where we provide world-class multi-disciplinary training.
 
This Research Associate position is available for fourteen months to work with Dr Guy-Bart Stan in the Department of Bioengineering and the new EPSRC Centre for Synthetic Biology and Innovation at Imperial College London. In this position, the successful applicant will work in a fast-paced dynamic environment performing studies at the interface between control engineering and synthetic biology. The research conducted will aim to build foundational methods for the optimal control of synthetic biology gene circuits using a data-based approach, mainly through the application and novel development of reinforcement learning methods.
 
Applicants should have a PhD in Computer Science, Control Engineering, Machine Learning, Computational Biology or closely aligned disciplines, or an equivalent level of professional qualifications and experience. Previous experience in nonlinear dynamical systems, reinforcement learning and optimal control (sequential decision making / multi-stage stochastic programming), deterministic and stochastic modelling of gene expression, or equivalent research, industrial or commercial experience is highly desired. Previous research experience in synthetic biology will be advantageous but is not a requirement; this project is an excellent opportunity for a skilled individual to cross-over into the exciting field of synthetic biology.

See below for a brief project description.

Job description and Person Specification: Job Description and Person Specification

Job Title: Research Associate - Data-based optimal control of synthetic biology gene circuits
Department/Division/Faculty: Department of Bioengineering, Centre for Synthetic Biology and Innovation, Imperial College London, South Kensington Campus
Salary: GBP 31,300 - 36,770  per annum
Fixed-term appointment for 14 months
Closing Date for applications: 15 March 2012 (midnight GMT)
The position needs to filled by June 2012
How to apply: Applicants should apply online by following this link. If this link does not work please visit http://www3.imperial.ac.uk/employment, select "Job Search", and then enter the job title "Data-based optimal control of synthetic biology gene circuits" or vacancy reference number EN20120027FH into "Keywords". Complete and upload an application form as indicated in the "How to apply" section. Once completed, please do not forget to upload your application form prior to submitting your application. In addition to the application form, please also attach a CV, a brief expression of interest including a short description of your past activities and goals, and a scanned copy of your PhD certificate.

Brief project description:
Synthetic Biology aims at the engineering of biological systems. Its most prominent application is the rational modification or (re-)design of living organisms, ideally in a way akin to the engineering of man-made devices, for their efficient use in sectors such as energy, biomedicine, drug production and food technology. The availability of control mechanisms that can ensure robust and optimal operation of engineered systems is one of the key factors behind the tremendous advances in engineering fields such as transportation, industrial production and energy. However, in the case of engineered biosystems, their accurate control must typically overcome two important hurdles: uncertainty and noise. Uncertainty arises from a high number of components that interact in a nonlinear (and often unknown) manner, and makes it often extremely hard to build accurate mathematical models of their behaviour. Noise, on the other hand, is ubiquitous in cellular systems since the environmental conditions in which they operate typically vary unpredictably and gene expression is inherently a stochastic process.

In this project, we will investigate the possibility of automatically learning to optimally control synthetic biology gene networks from input-output data collected from these gene networks, i.e. without using a mathematical model built a priori. In particular, we will develop algorithms that allow computer-based systems to autonomously learn how to vary the inputs of given gene networks so as to optimise their performance defined in terms of the time evolution of their measured outputs. The control strategies learned by our methods will take into account noise and uncertainties in the data and will be developed to be robust with respect to these. Such data-based strategies are analogous to, for example, the way we drive our cars: without any a priori mathematical model of the car behaviour on the road, we can effectively learn when and by how much to steer, accelerate and break (inputs) based on our observations of the car's position and velocity on the road (outputs) so as to, for example, minimise our lap time around an unknown track.

The algorithms we will develop will allow users to define the desired behaviour and performance objectives and will compute input-scheduling strategies that allow these objectives to be satisfied. The project will build on methods that we have developed and successfully applied to the optimal control of nonlinear systems in noisy environments, e.g., data-based optimal drug-scheduling for HIV infected patients (http://www.bg.ic.ac.uk/research/g.stan/CDC_2006.pdf). The use of such purely data-based optimal control methods is particularly important in synthetic biology applications where the system to be controlled is typically poorly characterised and model uncertainties prevail, yet large amount of high-throughput input-output data are available or can be extracted.

For further information regarding the post please contact Dr Guy-Bart Stan (g.stan@imperial.ac.uk).