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13 February 2017: CI²MA researchers were awarded funding from Fondecyt Regular

For second consecutive year, two researchers from the Center for Research in Mathematical Engineering, CI²MA, Universidad de Concepción, were awarded funding by Concurso Nacional de Proyectos Fondecyt Regular 2017, a governmental initiative executed by the National Commission for Scientific and Technological Research, Conicyt.

Academicians Raimund Bürger and Luis Mauricio Castro, o postulated projects Modeling, numerical analysis and scientific computing for convection-diffusion-reaction equations and coupled flow-transport problems (Nº 1170473) and Extending censored regression and mixed-effects models under Nonstandard assumptions for complex longitudinal data (No. 1170258), respectively.

This state funding program covers expenses for research projects of between 2 and 4 years, with an annual contribution of up to $57,000,000.- after an evaluation with quality criteria and merit of the research proposed by the postulant academics. This coverage includes expenses related to the remuneration of the responsible investigator, in addition to the fees of a co-researcher; fellowships for doctoral or undergraduate theses; fees for technical staff; and funds for research collaborations inside or outside the country; among other benefits .

The research led by Professor Bürger addresses four research topics, through which it is expected to develop efficient numerical schemes, and apply them to real problems, for two different types of models defined by partial differential equations (PDEs) . Among the types of problems to be studied are the so-called convection-diffusion-reaction and the coupled flow transport ones. The results may be applied, for example, in continuous sedimentation models development with reactions in mineral processing and wastewater treatment, as well as in modeling studies for epidemiology and mathematical biology, among others.

On the other hand, Professor Castro's research seeks to generate more flexible statistical models than those currently available for the treatment of univariate and multivariate censored data, through the use of parametric distribution families and non-parametric strategies which are based in stochastic processes capable of adapting influential observations, considering complex data characteristics such as the presence of errors in the measurement, asymmetry, excess of kurtosis, among others. These results, Castro hopes, can be applied in econometric analysis, clinical trials and biostatistics, among other uses.

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