Post-Doctoral Research Associate position on "In-vivo Optimal Experimental Design”
Dr. Menolascina’s laboratory at the University of Edinburgh is looking to recruit a bright Post-Doctoral Research Associate to work on the automatic identification of mathematical models of biomolecular networks in live cells.
As part of this project, the successful candidate will:
- build a cyberphysical system, combining microfluidics and videomicroscopy, where cells can be probed with computer controlled chemical stimuli and their response tracked over long experiments;
- develop a “supervisor algorithm” that combines computer vision methods and on-line optimization (Optimal Experimental Design/Parameter Estimation) to continuously update the model of the network of interest, identify its weaknesses, and design/apply an input optimised to refine it;
- close the loop between the in-vivo (a) and in-silico (b) components to iterate this procedure until a satisfactory model has been obtained.
The ideal candidate has a background in engineering/physics/mathematics and previous experience with techniques/protocols in microbiology and microscopy. Experience with microfluidic device fabrication is desirable.
Duration:: 3 years
Deadline: Applications are accepted/evaluated on a rolling basis until September 30th, 2017.
Application: Please send CV and 2 letters of reference to Filippo.firstname.lastname@example.org
PhD Student position on "In-vivo Optimal Experimental Design”
A fully funded1 PhD student position is available in Dr. Menolascina’s laboratory at the University of Edinburgh, to focus on the development of a real-time algorithm for the automatic identification of mathematical models of biomolecular networks in live cells.
Mathematical models are at the heart of Systems and Synthetic Biology, yet obtaining them remains an exceedingly laborious and expensive activity. This is mainly due to how model identification experiments have been carried out so far in biology: mainly limited by technology, we have used “step” or “pulse” stimuli, even though it has long been known these led to lowly-informative experiments. How can we then maximize the information associated to an experiment in Systems and Synthetic biology?
Optimal Experimental Design (OED) allows us to accomplish this by continuously estimating the best stimulus to apply to the cells to extract the largest amount of information from an experiment. In doing so OED allows to minimize time required to identify a mathematical model and reduce experimental costs. We have now conceived a new experimental setup that combines microfluidics and fluorescence microscopy and allows us, for the first time, to implement arbitrarily complex stimuli, like the ones required to exploit OED in biology. Yet a challenge remains to be solved: the search of the optimal stimulus is extremely computationally expensive which poses a severe limitation given the real-time nature of the problem. General Purpose GPU computing, the most advanced form of computation available, will be exploited to solve this problem as it allows a seamless parallelization of the optimization task (the successful candidate will be provided with specific training).
As part of this project you will design an algorithm that will automatically infer Ordinary Differential Equations models of biological systems and work on the first implementation of an Optimal Experimental Design algorithm on our CUDA enabled computerized microscope.
Duration: 3 years.
Deadline: Applications are accepted/evaluated on a rolling basis until October 31st, 2017.
Application: Please send CV to Filippo.email@example.com
Additional details: https://www.findaphd.com/search/projectdetails.aspx?PJID=69330