Research Technician in Applied Statistics

Aug 15, 2019
Sep 14, 2019
Organization Type
University and College
Full Time
Estimation of the distribution of the accumulated cost per patient in the presence of censored data

This project deals with the estimation of the distribution of the accumulated cost per patient according to diseases, the received medical allowance, etc. The objective is not to estimate the, e.g., average cost, but to estimate the entire distribution. This will allow us to fully characterize the population of interest, which will provide information that we seek to further explore and study in detail. In addition to defining the methods for estimating the accumulated cost per patient, the project aims at defining and automating the workflow that will allow obtaining the data in the appropriate format for the subsequent analyses. In doing so, we seek to allow the routine use of the proposed methodology.

About BCAM Knowledge Transfer Unit (KTU):

The aim of the KTU is to develop mathematical solutions for scientific challenges based on real-life applications. One of BCAM's most important missions is to spread knowledge and technology in the industry and society in general. It is critical for the Basque Center for Applied Mathematics to transfer the obtained research results to sectors as biosciences, health, energy, advanced manufacturing, telecommunications and transport, including local, national and international entities.

There is a moving allowance for those researchers that come from a research institution outside the Basque Country from EUR 500 to EUR 1.000 gross.

The preferred candidate will have:
  • Strong background in Statistics and Mathematics.
  • Demonstrated knowledge in advanced statistical methods and/or machine learning techniques.
  • Good programming skills in R and/or Python.
  • Research experience in interdisciplinary applications (e.g.: Health, Energy).

The selected candidate must have applied before the application deadline online at the webpage.

The candidates that do not fulfil the mandatory requirements will not be evaluated with respect to their scientific profile.

Required documents:
  • CV
  • Letter of interest
  • 2 recommendation letters (desirable)

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