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Förslaget inkom 2004-09-29

Data redundancy ¿ the new challenge in chemometrics

OBS! ANSÖKNINGSTIDEN FÖR DETTA EXJOBB HAR LÖPT UT.
Today¿s platforms for measuring chemistry are yielding an abundance of data (GB/sample). One of the major obstacles in data modelling is the size of the data and the occurrence of noise and/or unwanted data artefacts. This work aims at constructing pruning methods for collected data with the aim of reducing the data size and effectively cleaning the data from noise without loosing information. The proposed approach is incorporate system level knowledge and statistics as priors for the reduction. The target system for the proposed work is LC/MS (Liquid Chromatography/Mass spectrometry) data and the performance is to be elucidated by modelling the data with methods like PCA, PLS, PARAFAC. The data are already acquired so no instrument or sample handling is necessary. The work is in the context of metabolite tracing in the area of drug development but it is expected that the results should be generally applicable. The question is one of major importance for the academic/industrial community and represents one of the key issues to be addressed for the area of chemometrics over the next decade.

The applicant should, preferably, have a strong interest in analytical chemistry and statistics. Furthermore; basic knowledge in programming (Matlab) and chemometrics are advantageous. The work is to be performed at the Stockholm University.


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