IBS: Postdoctoral Research Assistant

School of Biological and Chemical Sciences, Queen Mary, University of London

Postdoctoral Research Assistant, Ref: 11144/KK

A postdoctoral applicant is sought with an interest in ecological modelling. The successful applicant will be developing coupled, process-based, models of ecological systems. You will be working under the supervision of Professor M.R. Evans (QMUL). The project is based at Queen Mary, University of London.

This is a three-year, full time position, starting in October 2011 or as soon as possible thereafter. The salary is in the range of £30,350-£39,786 per annum including London allowance. Salary will be according to qualifications, skills and experience. Benefits include 30 days annual leave, final salary pension scheme and an interest-free season ticket loan.

Candidates should hold a PhD in a relevant area of ecology or computer science or have equivalent experience. Training can also be given where needed and there will be opportunities to attend international conferences.

Candidates must be able to demonstrate their eligibility to work in the UK in accordance with the Immigration, Asylum and Nationality Act 2006. Where required this may include entry clearance or continued leave to remain under the Points Based Immigration Scheme.

Further details and an application form can be obtained from the Human Resources website on: http://www.hr.qmul.ac.uk/vacancies. For further information about the School, please see http://www.sbcs.qmul.ac.uk.

Informal enquiries can also be made to Professor. M.R. Evans at m.r.evans@exeter.ac.uk

Completed application forms together with a copy of your CV, quoting reference number 11144/KK, should be returned to Ms Sunita Devi-Paul, School of Biological & Chemical Sciences, Queen Mary, University of London, Mile End Road, London, E1 4NS, or by e-mail: sbcs-vacancies@qmul.ac.uk.

The closing date for applications is 23rd June 2011 at 4.00pm. Interviews will be held 12th July 2011.
Unfortunately, we are unable to reply to those applicants who have not been short listed and invited for interview. However, we would like to thank all candidates for their applications and interest.

Valuing Diversity & Committed to Equality

PDRA in Systems Ecology
We are facing a period of unprecedented environmental change. While climatological modelling provides robust estimates of the predicted changes in temperature and precipitation patterns it is clear that few will notice the direct changes in climate against the background of naturally variable weather patterns. In contrast, many will be affected by the indirect effects of climate change through the impact it has on the natural world. There are going to be changes in biodiversity, ecosystem services, landscape quality and the performance of agricultural land. At present, in contrast to our level of comprehension of the expected physical changes, we have a relatively poor understanding of the scale and nature of the impacts of climate change on the biological world. To answer crucial questions about the impact of climate change on biological systems we must be able to predict the state of such systems in future, novel conditions. Prediction in ecology is typically made using models, and conventionally ecologists have used models that are good at describing observed patterns but are poor at predicting future behaviour, especially under conditions outside the bounds of observation (such as will be inevitable as climate change progresses). Ecology must develop the ability to produce robust, accurate predictions of specific systems in novel environments. A process-based, systems approach is essential to provide this ability and has been used successfully in the related fields of climatology and molecular systems biology. In this project I propose to apply large-scale computational techniques to existing long-term ecological datasets (by analogy with molecular systems biology these are the ecological equivalent of high-throughput data). The aim of this project is to establish a systems-approach to ecological prediction and forecasting. The objective will be to apply existing a model that describes forest dynamics such as SORTIE or ED (Pacala et al. 1996; Moorcroft, Hurtt & Pacala 2001; Purves & Pacala 2008; Purves et al. 2008; Strigul et al. 2008) to data from UK forests. We then intend to couple this model to individual-based models of herbivores using the trees as a food resource, the approach would be similar to that used in a model of forests and human clearance in a Himalayan Valley (Bithell & Brasington 2009). The intention would be to project this model into the future by allowing it to be forced with climate change data.
We are looking for a PDRA with interests in ecology and experience using and developing large-scale computer models. This post is initially available for 3 years starting 01/10/11, and is intended as research support for Prof. M.R. Evans whilst acting as head of the School of Biological and Chemical Sciences. For informal discussions please contact m.r.evans@exeter.ac.uk.
Bithell, M. & Brasington, J. (2009) Coupling agent-based models of subsistence farming with individual-based forest models and dynamic models of water distribution. Environmental Modelling and Software, 24, 173-190.
Moorcroft, P.R., Hurtt, G.C. & Pacala, S.W. (2001) A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecological Monographs, 71, 557-586.
Pacala, S.W., Canham, C.D., Saponara, J., Silander, J.A., Kobe, R.K. & Ribbens, E. (1996) Forest models defined by field measurements: estimation, error analysis and dynamics. Ecological Monographs, 66, 1-43.
Purves, D. & Pacala, S. (2008) Predictive models of forest dynamics. Science, 320, 1452-1453.
Purves, D.W., Lichstein, J.W., Strigul, N. & Pacala, S.W. (2008) Predicting and understanding forest dynamics using a simple tractable model. Proceedings Of The National Academy Of Sciences Of The United States Of America, 105, 17018-17022.
Strigul, N., Pristinski, D., Purves, D., Dushoff, J. & Pacala, S. (2008) Scaling from trees to forests: tractable macroscopic equations for forest dynamics. Ecological Monographs, 78, 523-545.