项目介绍

2026年4月19日

For a collaborative project (“FuturoChrom”) between the University of Leuven (KU Leuven) and the Free University of Brussels (VUB), focusing on developing next‑generation computational models for predicting chromatographic retention behavior, the division of Pharmaceutical Analysis of the Department of Pharmaceutical and Pharmacological Sciences of KU Leuven and the Department of Chemical Engineering of VUB, are looking for a highly motivated PhD candidate.
Within Pharmaceutical Analysis, the research group of the PhD promoter Prof. Deirdre Cabooter works on the fundamental investigation of liquid chromatographic supports for the analysis of both small and large molecules, the implementation of multidimensional liquid chromatography techniques in combination with mass spectrometry for environmental, pharmaceutical and biomedical applications and the development of automatable method development strategies for complex samples.
Within the Department of Chemical Engineering, the research group of the PhD co-promoter Gert Desmet focuses on the development of faster and more sensitive separation methods, mostly in chromatography. To base this work on firm theoretical grounds, the group also does important work on the measurement and modeling of flow effects in microfluidic and chromatographic systems, for which it received wide international recognition.

Website van de eenheid

Project

Chromatographic problem solving, commonly referred to as method development, is hugely complex, given the many chemical parameters that need to be optimized and the sensitive relation between the value of these parameters and the elution time of the individual sample compounds. While the current human-reasoning and model-based approaches often fail due to the lack of accurate models, machine learning-based problem solving is now revolutionizing almost every field of science and technology. FuturoChrom aims at developing model-free, purely reinforcement learning-powered algorithms as well as hybrid approaches, combining either reinforcement learning or deep learning (Graph Neural Networks) with human-based modelling, for fully flawless and autonomous method development. The developed algorithms will be compared to the current state-of-the-art in method development using samples provided by some of Flanders’ most demanding industrial chromatography labs. To cover a statistically relevant number of samples, a large-scale in silico comparison will be made as well. For this purpose, we will also pioneer in establishing in silico test grounds that are as realistic as possible.

Profile

The candidate ideally has a Master’s degree in (Analytical) Chemistry, Pharmaceutical Sciences, Bio- or Chemical Engineering, or a related field, with a strong interest in analytical chemistry. A good knowledge of and hands-on experience with (U)HPLC are an absolute must, while familiarity with mass spectrometry (MS) is a plus. We are looking for someone with a genuine interest in machine learning and hands‑on experience with Python or other scientific programming tools. Candidates must have a strong analytical mindset and willingness to work across experimental and computational domains, be proficient in oral and written English, must have excellent communication skills, and be team-oriented, proactive and results driven. 
The position can start as soon as you are available. Interested candidates should send their CV and a motivation letter using the KU Leuven online application tool.

Offer

We offer a PhD position in a stimulating environment at a top European university in a well-equipped, experienced and internationally oriented research unit and this for a fixed term of 1 year, renewable – subject to a positive evaluation – for a maximum of 4 years in total. You will be based at the Department of Pharmaceutical and Pharmacological Sciences at the Gasthuisberg Campus in Leuven, but you will also closely collaborate with the Department of Chemical Engineering of the Vrije Universiteit Brussel, working at the interface of analytical chemistry and machine learning.

Interested?

For more information please contact Prof. dr. Deirdre Cabooter, mail: deirdre.cabooter@kuleuven.be.You can apply for this job no later than May 29, 2026 via the online application tool