爱丁堡大学疟疾研究博士后岗位
日期：2007-10-18
来源：丁香人才网
InstitutionUniversity of EdinburghPositionHost-parasite interactions elucidated by McMC based Bayesian inferenceLocationEdinburghSalary27466 - 32796 英镑Date PostedOct 17th 2007Closing Date Nov 7th 2007 ...
Institution
University of Edinburgh
Position
Host-parasite interactions elucidated by McMC based Bayesian inference
Location
Edinburgh
Salary
27,466 - 32,796 英镑
Date Posted
Oct 17th 2007
Closing Date
Nov 7th 2007
Description
Supervisors: Nick Savill, Andrew Read
Term: 2 years
Malaria is a globally important disease. The blood stages of malariainfections are responsible for pathology and transmission, and are thuskey components of the clinical outcome for individual patients and forthe epidemiology and evolution of the parasite. Understanding thenatural determinants of infection dynamics, and the effect ofinterventions such as chemotherapy and vaccination on these, is key tounderstanding and controlling malaria.
Mathematical models can aid in determining the relative importance offactors regulating infection dynamics, how these factors interact, andin predicting the outcomes of possible interventions. Our key goal is todevelop computational methods to overcome the principle challengepreventing the realisation of this potential: a very large number ofmathematical models can be fitted to any data set. Prof. Read’s lab hasgenerated over 1,000 time series of malaria kinetics in a diverse rangeof infections in mice. We propose to construct a combinatorially largenumber of mechanistic mathematical models by combining sets ofhypotheses about the actions and interactions of host and parasitefactors. Models will be developed, fitted and their adequacy assessedusing McMC based Bayesian inference.
This exciting project will yield new insights into the biologicalinteractions between malaria parasites and their mammalian hosts inearly bloodstage infections, as well as generating a theoretical toolboxfor application to analogous issues relevant to studies of many otherinfectious agents, including human malaria. It will give importantadvances into the rational choice of drugs and vaccines and will be usedto determine which new experiments will most usefully further reduce thenumber of surviving models.
We are looking for exceptional candidates with a strong appliedcomputational or mathematical background. Experience of Bayesianinference, malaria research, mathematical biology and interaction withbiologists would be beneficial, but not necessary.
For more details and to apply visit www.jobs.ed.ac.uk. Reference number3008069. Or contact me directly.
Contact
Email: nick.savill@ed.ac.uk
Website: homepages.ed.ac.uk/nsavill
University of Edinburgh
Position
Host-parasite interactions elucidated by McMC based Bayesian inference
Location
Edinburgh
Salary
27,466 - 32,796 英镑
Date Posted
Oct 17th 2007
Closing Date
Nov 7th 2007
Description
Supervisors: Nick Savill, Andrew Read
Term: 2 years
Malaria is a globally important disease. The blood stages of malariainfections are responsible for pathology and transmission, and are thuskey components of the clinical outcome for individual patients and forthe epidemiology and evolution of the parasite. Understanding thenatural determinants of infection dynamics, and the effect ofinterventions such as chemotherapy and vaccination on these, is key tounderstanding and controlling malaria.
Mathematical models can aid in determining the relative importance offactors regulating infection dynamics, how these factors interact, andin predicting the outcomes of possible interventions. Our key goal is todevelop computational methods to overcome the principle challengepreventing the realisation of this potential: a very large number ofmathematical models can be fitted to any data set. Prof. Read’s lab hasgenerated over 1,000 time series of malaria kinetics in a diverse rangeof infections in mice. We propose to construct a combinatorially largenumber of mechanistic mathematical models by combining sets ofhypotheses about the actions and interactions of host and parasitefactors. Models will be developed, fitted and their adequacy assessedusing McMC based Bayesian inference.
This exciting project will yield new insights into the biologicalinteractions between malaria parasites and their mammalian hosts inearly bloodstage infections, as well as generating a theoretical toolboxfor application to analogous issues relevant to studies of many otherinfectious agents, including human malaria. It will give importantadvances into the rational choice of drugs and vaccines and will be usedto determine which new experiments will most usefully further reduce thenumber of surviving models.
We are looking for exceptional candidates with a strong appliedcomputational or mathematical background. Experience of Bayesianinference, malaria research, mathematical biology and interaction withbiologists would be beneficial, but not necessary.
For more details and to apply visit www.jobs.ed.ac.uk. Reference number3008069. Or contact me directly.
Contact
Email: nick.savill@ed.ac.uk
Website: homepages.ed.ac.uk/nsavill
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