Vacancy for highly motivated PhD Researcher:

“Maximal use of a priori sample knowledge to decide on optimal combinations of analytical separations”

For a multidisciplinary project in collaboration with the Free University of Brussels, Ghent University and the University of Liège focusing on Mining Chemical Data in Complex Samples, the ANALOG research group of the University of Liège (ULiege) is looking for a highly motivated PhD candidate.

The PhD position will be co-promoted by Profs. Philippe Hubert and Marianne Fillet. Prof. Hubert is currently working on risk-based strategy for the robust development and optimization of ultra-high performance liquid chromatography (UHPLC) and supercritical fluid chromatography (SFC) methods in combination with simple quadrupole mass spectrometry (MS) for pharmaceutical analysis as well as counterfeit or poor quality medicines. Recently, his group has initiated an innovative strategy to combine optimization and validation steps on the framework of analytical quality-by-design (AQbD) strategy allowing a systematic and scientific approach of the entire analytical method lifecycles. Prof. Marianne Fillet mainly works on the development of innovative and robust analytical methods (DoE and validation) for the separation and quantitation of (bio)pharmaceuticals in GMP environment and disease biomarkers in complex matrix (cells, tissues, biological fluids). Over the last 15 years, Fillet’s research group focuses more particularly on capillary electrophoresis, UHPLC and nano-LC hyphenation with different kinds of mass spectrometers (IT, TQ, IMS-Q-TOF).

 

Project

Within this project, the PhD researcher will aim at predicting the general retention behavior of a given sample based on a priori descriptors of its composition. These descriptors can vary from relatively simple parameters up to the very specific descriptors used in advanced QSRR-methods. Furthermore, PhD researcher will also investigate the possibility to extend this strategy with a Bayesian approach to assess and manage the uncertainty of the predictive model. The predictive models will also be computed using class representative compounds and will be challenged in silico against well-known sample sets to check how well the model can predict the general spread of the molecules. Subsequently, the predictive models will be tested experimentally considering an analytical quality-by-design (AQbD) approach combining design of experiments (DoE) and design space (DS). Finally, this strategy will be extended to combine different separation modes allowing resolving complex mixture.

 

Profile

The candidate ideally has a Master in Bioinformatics, Bioengineering, Chemistry, Statistics or Pharmaceutical Sciences, with a strong interest in analytical chemistry and computer sciences. The candidate is familiar with experimental testing as well as chemometrics in the framework of establishment of predictive models. Good algorithm programming skills would be an asset. Candidates must be proficient in oral and written English, must have excellent communication skills, multi-tasking skills, and be team-oriented, proactive and results driven. The candidate will work as a member of a large research project that will involve frequent interactions with external researchers.

The position can start immediately for 4 years (2+2) period.

Interested candidates should send their CV and a motivation letter to following email address: Marianne.fillet@uliege.be , chubert@uliege.be

 

Offer

The Center for Interdisciplinary Research on Medicine (CIRM) offers a PhD position in a stimulating environment at a top European university in a well equipped, experienced and internationally oriented research unit. You will be based at Department of Pharmacy at the Centre Hospitalier Universitaire (CHU) Campus in Liège.