Ph.D. studentships available.
Train with Andrew Millar and the lab in SynthSys, Edinburgh's Centre for Synthetic and Systems Biology, to deliver world-class research with an interdisciplinary perspective.
Students with first-class academic qualifications in a numerate discipline can contact firstname.lastname@example.org to discuss projects in the areas above. Funding application deadline in mid-December each year, for Ph.D.'s starting in the following October; enquiries welcome at any time from candidates with funding. Only online applications can be accepted for most studentships:
Predicting Plant Growth, from Genes to Organism. (funding deadline passed)
We invite applications to a groundbreaking PhD studentship, which provides dual-expertise training in the interdisciplinary environment of SynthSys, with experience at Simulistics Ltd. The project is fully-funded for UK and resident EU students, through a BBSRC industrial CASE studentship.
Understanding the growth of a plant in a changing environment is demanding, because plant development and metabolism respond sensitively to the local conditions. We have linked Crop Science and Systems Biology approaches to understand whole-plant growth, in the first ‘Framework Model’ of the laboratory model plant Arabidopsis thaliana. The model predicts whole-plant biomass, from detailed molecular mechanisms, and was recently validated in independent experiments. This project will develop the next-generation model, both as a tool for fundamental biology, and to enable synthetic biology designs that take account of the complex regulation in the plant host. You will be trained to use a range of cutting-edge models, building on the concrete example of our Framework Model. The model will be extended to represent larger, molecular networks that control biomass under a wider range of environmental conditions, with international collaborators and Simulistics’ Simile software. You will validate the model in new experiments, using Arabidopsis mutants and environmental control will test the model’s predictive power, and to disseminate the models in the international research community.
Student profile: background in Biology, Geoscience, Agricultural Engineering or a suitably numerate discipline (e.g. computer science, engineering, applied maths or physics). Computer skills essential; programming experience desirable but not essential.
The supervisory team: Prof. Andrew Millar FRS, School of Biological Sciences (main supervisor); Prof. Vincent Danos, School of Informatics, Director of SynthSys; Dr. Robert Muetzelfeldt, Simulistics Ltd.
Online applications, deadline passed: http://www.ed.ac.uk/studying/postgraduate/degrees?id=12&cw_xml=details.php.
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