Current practice in life sciences is that many analytical platforms are used to get a complete overview of e.g. the metabolome. Measurements from GCMS, LCMS-positive mode and LCMS-negative mode, NMR, direct-MS and others are combined to gain the most complete snapshot of the metabolome. Often there are also measurements on genetic markers, proteins and physiological and sensory traits. Combing all this information from the different data blocks is not trivial. Most statistical methods do not exploit the structure and relationships between and within the different data blocks.
In this project, you will shortly take inventory on current methods from literature and apply them to life sciences data sets measured on multiple platforms.
An interest in statistical methods, programming and life sciences (in random order). Familiarity with programming would help (e.g. matlab, r, python).