Welcome to Paul, the first NEXTSTEP PhD student
Nextstep is a Horizon Europe-funded doctoral training programme aimed at preparing the next generation of researchers to tackle sustainability and innovation challenges. Aligned with the goals of the European Green Deal and the Sustainable Development Goals, Nextstep connects research infrastructures, academia, and industry to foster interdisciplinary collaboration and cutting-edge research.
Its aim is to recruit 36 outstanding doctoral candidates who will gain access to state-of-the-art research infrastructures and cutting-edge techniques in neutron, X-ray, and electron microscopy - collaborating with leading experts across Europe and make a real impact on sustainable development and innovation.
Its first PhD student, Paul Grosskopf, arrived in September 2025 at the ILL, where he joined the Scientific Computing group. His academic training will be co-supported by the FAU Erlangen-Nürnberg in Nuremberg (Germany).
Paul’s PhD project focuses on the “Development of a combined SAXS/SANS/PCS analysis method for studies on the mesoscopic structure of LNP-based mRNA delivery systems”, supervised by Lionel Porcar and Miguel Gonzalez (ILL) and Tobias Unruh (FAU).
His work will be conducted at the instrument D22, in close collaboration with a second PhD student based at FAU. Together, their research will advance the understanding and optimisation of lipid nanoparticle (LNP) formulations for biomedical applications.
Paul GROSSKOPF, the first NEXTSTEP PhD student arrived at the ILL in September 2025.
Paul completed his Master's degree in Chemistry with a focus on computational chemistry at Georg-August University in Göttingen, Germany. During his studies, he developed skills that he hadn’t yet been able to fully apply in a research setting.
"Which Is why I seeked to pursue a PhD! My interests in data fusion, statistics and machine learning drew me to this project, which combines SANS, SAXS, and PCS to investigate the structure of lipid nanoparticles. Large-scale facilities provide unique opportunities for experimentation and foster collaborations with people from diverse backgrounds, broadening perspectives and knowledge."
He smiles broadly and adds, "Everything has gone very well so far. Everyone has been very patient in explaining what I need to know to get started. The ILL has a lot more to offer beyond this fantastic collaboration."
Miguel Gonzalez's thoughts on the context and expectations around this project
The use of lipid nanoparticles to deliver drugs is very recent and, therefore, the understanding of their structure–function relationship is still poor.
We are trying to better understand the mesoscopic structure of lipid nanoparticles used for mRNA drug delivery. As a single type of structural measurement is not enough to provide a full picture of both their structure and their size distribution, this requires the combination of different experimental measurements—in particular, small-angle neutron and x-ray scattering and photon correlation spectroscopy.
The challenge thus consists in developing the necessary analysis tools to facilitate the combined analysis of results provided by these different experimental techniques, in order to identify the unique structural model that best describes the full dataset and the structure and size distribution of the lipid nanoparticles employed in different drug formulations.
A second goal of this project is to benefit from recent advances in machine learning to accelerate and automate this process as much as possible, offering a way to understand the drug delivery process better and faster.
We hope that this project will bring significant insights, helping the health industry to improve the stability, medical efficiency, and range of applications of lipid nanoparticle-based drug formulations.
Paul is the very first of 20 PhD candidates in the Nextstep Programme’s inaugural cohort. More students will be joining in October, and a second cohort is already planned for 2026, with new PhD projects to be announced in early spring - to be continued!