[Comp-neuro] PhD student position on "Spatial Statistics Applied to Neuroscience" at UPM, Madrid

Pedro Luis López Cruz pedro.lcruz at upm.es
Wed Jul 25 09:11:57 CEST 2012


The Computational Intelligence Group at the Computer Science School of 
the Technical University of Madrid (UPM, 
http://www.upm.es/internacional) is looking for a PhD student. He/She 
will be working on "Spatial Statistics Applied to Neuroscience" within 
the Cajal Blue Brain project, the Spanish representation of the 
international 'Blue Brain Project'. The candidate will join Programa de 
Formación Homologado de Becas-Contrato (Officially Approved Education 
Program of the Technical University of Madrid).

*Details *

  * Topic: Spatial statistics in neuroscience, with special emphasis on
    spatial point processes
  * Type of contract: Programa de Formación Homologado de Becas-Contrato
    de la UPM / Officially Approved Education Program of the Technical
    University of Madrid
  * Length: 4 years (2 scholarship / 2 contract) based on full-time
    employment
  * Salary:  the minimum fixed by the Program (1200 EUR/month) + bonus
    due to achievements
  * Place: Facultad de Informática (UPM)
  * Predicted date to start: November 1, 2012
  * Closing date for applying: September 15, 2012
  * Send CV and letter of application to: mcbielza at fi.upm.es,
    pedro.larranaga at fi.upm.es
  * Computational Intelligence Group website: http://cig.fi.upm.es/
  * Cajal Blue Brain website: http://cajalbbp.cesvima.upm.es/


*Requirements*

  * Academic degree: Graduate/Engineer
  * Degree in: Statistics or Mathematics (preferably)/Computer Science
  * Experience: Knowledge of R package will be of merit
  * Languages: English (Spanish is not required)
  * Other: Knowledge of Bayesian networks will be of merit


*Tasks *

  * Analysis of 3D spatial distribution of different neuronal structures
    (synapses, spines...) from electron microscope images.
  * Incorporation of marks: symmetric and asymmetric synapses, shape and
    size of spines...
  * Bayesian networks to model spatial distributions with different
    kinds of marks.


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