PhD in Data-Driven Modeling of Homogeneous Catalysts

PhD in Data-Driven Modeling of Homogeneous Catalysts

Job description

Among the most challenging to develop catalytic reactions are stereoselective processes. Typically, a family of catalysts is explored based on a preliminary hypothesis. After initial experimental results, further research is guided by trial and error with the goal of deriving intuitive trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and used to establish such a model. A new set of catalysts can then be predicted and tested. Subsequently, the new data is fed back into the model to improve its prediction capabilities.

Chiral bidentate ligands belong to the most widely used co-catalysts in asymmetric catalysis. However, the synthesis of systematic libraries based on chiral bidentate ligands can be laborious, rendering data-driven bidentate ligand design challenging. In principle, one molecule of a bidentate ligand can be replaced with two molecules of monodentate ligands, enabling the use of combinatorial mixtures, which has been shown to lead to highly selective methodologies. This approach greatly simplifies the synthetic accessibility of viable ligand systems, but leads to a combinatorial explosion of candidates. In this project, we will develop predictive data-driven models describing the catalytic activity and stereoinduction capabilities of ligand mixtures, allowing for their efficient experimental optimization.

The goal of this project is to: 1) develop dedicated representations for catalyst mixtures; 2) apply the developed representations to building predictive models for catalyst activity and selectivity; 3) employ the developed representations in closed-loop catalyst optimization workflows.

This position will be part of the Marie-Sklodowska-Curie Doctoral Network CATALOOP. This network aims at the development of powerful and readily applicable workflows for data-driven development of stereoselective catalysis. As a main training goal, we want to educate researchers in comprehensive data-driven experimental approaches for realizing challenging asymmetric catalytic methods. This network brings together academic research groups with expertise in experimental catalyst development and theoretical groups skilled in computational chemistry and data-driven approaches to develop new catalytic asymmetric reactions. World-leading industrial partners with a wide range of interests will provide advice on which approaches may have the most impact on industry and will also host the students in secondments.

Organization
Founded in 1614, the University of Groningen enjoys an excellent international reputation as a dynamic and innovative institution of higher education offering high-quality teaching and research. Flexible study programs and academic career opportunities in a wide variety of disciplines encourage the 31,000 students and researchers alike to develop their own individual talents. As one of the best research institutions in Europe, the University of Groningen has joined forces with other top universities and networks worldwide to become a truly global center of knowledge, situated in the vibrant city of Groningen in the north of The Netherlands.

Qualifications

The successful candidate for this position will have the following qualifications/qualities

• An MSc degree in chemistry or a related field.
• Very strong academic performance.
• Experience in molecular machine learning.
• Experience with the programming language Python.
• Experience in computational chemistry.
• Basic knowledge about homogeneous catalysis.
• Strong interest in combining computational and experimental chemistry.
• Excellent knowledge of written and spoken English.
• Excellent communication skills, especially for collaborating with others.
• Enthusiasm and initiative to perform creative research in an interdisciplinary setting.
• You must not have resided in the country of the PhD placement for longer than 12 months prior to the start of the network program in October 2025.

Applicants whose first language is not English must submit evidence of competency in English, please see the University of Groningen’s English Language Requirements for details.

Conditions of employment

The University of Groningen offers, in accordance with the Collective Labour Agreement for Dutch Universities

• A salary of € 2,901 gross per month in the first year, up to a maximum of € 3,707 gross per month in the fourth and final year, based on a full-time position (1.0 FTE).
• A gross holiday allowance of 8%.
• A gross 8.3% year-end bonus.
• A position for four years; you will get a temporary position of one year with the option of renewal for another three years; prolongation of the contract is contingent on sufficient progress in the first year to indicate that a successful completion of the PhD thesis within the next three years is to be expected.

The starting date is October 1st, 2025.

The University of Groningen strives to be a university in which students and staff are respected and feel at home, regardless of differences in background, experiences, perspectives, and identities. We believe that working on our core values of inclusion and equality are a joint responsibility and we are constructively working on creating a socially safe environment. Diversity among students and staff members enriches academic debate and contributes to the quality of our teaching and research. We therefore invite applicants from underrepresented groups in particular to apply.

Our selection procedure follows the guidelines of the Recruitment code (NVP) and European Commission's European Code of Conduct for recruitment of researchers.

We provide career services for partners of new faculty members moving to Groningen.

Information

For information you can contact:

• Robert Pollice, r.pollice rug.nl