Doctoral Research Associate (Wissenschaftliche*r Mitarbeiter*in, salary level E 13 TV-L)

42,500 students and 7,750 employees in teaching, research and administration, all working together to shape perspectives for the future – that is the University of Münster. Embedded in the vibrant atmosphere of Münster with its high standard of living, the University’s diverse research profile and attractive study programmes draw students and researchers throughout Germany and from around the world.

The Institute of Organic Chemistry in the Faculty of Chemistry and Pharmacy at the University of Münster is seeking to fill the position of a PhD student in the Faculty of Chemistry. The position is available from the earliest possible date, preferably by 1st April 2026, as a full (100 %) position limited to 3 years. This PhD position is part of the EU‑funded Marie Skłodowska‑Curie Doctoral Network on Low Data Machine Learning for Sustainable Chemical Sciences under Grant Agreement No.  .

As part of the Glorius Group at the University of Münster, we are a team of passionate researchers committed to shaping the future of molecular science. Our research bridges catalysis, functional molecule design, molecular machine learning and data‑driven discovery to address pressing scientific challenges. We are pioneers in the application of data science to challenges in (organic) chemistry, with over 15 years of interdisciplinary research. Our data science team develops smart screening strategies and machine learning tools to accelerate reaction discovery & analysis, thereby improving chemical understanding. With a strong publication record and a collaborative spirit, we offer an inspiring environment for PhD candidates aiming to grow scientifically. We love science, innovation, and shaping the future of digital chemistry – together with you.

Project Overview

This project is a collaboration between 13 academic and industrial organisations with 14 PhD students in total. The aim of LowDataML is to train a new generation of scientists at the interface of machine learning, chemistry and other fields. We propose a data‑science guided and ML‑driven screening and optimisation workflow to improve the generality of modern synthetic methods.

Our first objective is to identify structural patterns and scaffolds that are accessible by modern synthetic methods but are at the same time underrepresented in databases of bioactive molecules such as ChEMBL. To achieve this, we will perform direct substructure matching and 3D similarity searches to determine key reactions based on product motifs. Generality of a methodology is critical to allow the incorporation of a variety of substrates and functional groups. We will use an additive‑based screening approach to rapidly evaluate the generality of key reactions, followed by reoptimisation of promising reactions using a Bayesian optimisation algorithm. Batch selection strategies will allow parallel experimentation in 96‑well plates, with analysis by liquid and gas chromatography combined with UV, mass and flame ionisation detection. Optimised reactions will be applied in multi‑step diversity‑oriented synthesis of drug‑like compounds.

Expected Results

• Identification of underrepresented structural motifs accessible by modern synthetic methodologies.
• Assessment of the robustness of key reactions using an additive‑based screening approach.
• Development of a multi‑objective Bayesian optimisation algorithm for the reoptimisation of reactions.
• Application of the reoptimised key reaction in the synthesis of drug‑like compounds.

As a PhD student in this Doctoral network you will use machine learning (ML) for the optimisation of the generality of synthetic methods. You will start with the search for structural patterns and scaffolds that are accessible by modern synthetic methods but are at the same time underrepresented in databases of bioactive molecules. You will apply an additive‑based screening approach to identify key reactions which can be investigated further. The applications of ML as a tool for the optimisation of reaction yields will be developed and implemented. Optimised reactions will be run in parallel using a liquid handler to set and work up experiments in 96‑well plates. Analysis and quantification of experimental results will be performed using diverse chromatographic methods in combination with spectroscopic measurements. The research is embedded in collaborations with mainly three partners, with whom there is regular exchange, including through mandatory visits to the laboratories of the collaboration partners.

PhD candidate will have research stays at:

• Farm‑ID (iMed Lisboa, Portugal) – 4 months, extension of expertise in ML‑based reaction optimisation.
• AZ (AstraZeneca, Sweden) – 4 months, investigate the optimisation approaches’ utility in real‑world applications and the key reactions’ integrability in retrosynthesis tools.
• AC (Acceleration Consortium, University of Toronto, Canada) – 4 months, integrate the ML tools in SDL technologies.

You are excited to work at the intersection between chemistry, AI and software development. Working in an interdisciplinary team between scientists and non‑scientists motivates you. You are eager to develop and implement your own research ideas independently. Analytical thinking and a structured approach to problem‑solving are second nature to you. You enjoy taking the initiative and leading research projects, including mentoring students.

Applicants must hold a Master’s degree (or equivalent) in Organic Chemistry, Chemistry, Computer Science or related disciplines. Previous experience in machine learning, organic chemistry, molecular dynamics or computational chemistry and programming skills (Python, PyTorch, TensorFlow) are required. Excellent command of spoken and written English, communication skills and the ability to work in a collaborative international environment are essential.

Applicants must be in the first 4 years after obtaining their Master’s or Bachelor’s degree and must not have resided or carried out their main activity (work, studies, etc.) in Germany for more than 12 months in the 3 years immediately before the recruitment date. Applicants must not have obtained a doctoral degree yet. The salary is based on standard living, mobility and family allowances which are adapted to the respective country of recruitment.

Advantages for you

• Appreciation, commitment, openness and respect – these values are important to us.
• Care and childcare support through the Family Service Office to help you balance your private and professional life.
• Your individual, custom training and further education is a priority for us.
• Sports and health programmes ranging from Aikido to Zumba to support work‑life balance.
• Public sector benefits such as an attractive company pension scheme (VBL), a special annual payment and a job that is hardly dependent on economic fluctuations.

We provide a structured 36‑month PhD training programme within the Marie Skłodowska‑Curie Doctoral Network LowDataML (Low Data Machine Learning for Sustainable Chemical Sciences). The programme offers cutting‑edge research and training at the intersection of machine learning, computational chemistry, biophysics, bioinformatics and drug discovery.

We offer a stimulating and interdisciplinary research environment with access to state‑of‑the‑art computational and experimental facilities and a strong track record of collaboration between academia and industry. In addition to individual training‑through‑research, fellows will participate in network‑wide workshops, summer schools, transferable skills courses and international secondments at partner institutions.

The University of Münster strongly supports equal opportunity and diversity. We welcome all applicants regardless of sex, nationality, ethnic or social background, religion or worldview, disability, age, sexual orientation or gender identity. We are committed to creating family‑friendly working conditions. Part‑time options are generally available.

We actively encourage applications by women. Women with equivalent qualifications and academic achievements will be preferentially considered unless these are outweighed by reasons which necessitate the selection of another candidate.

Have we aroused your interest? Then we look forward to receiving your application by 2026‑01‑15 . Applications should be sent only by email and include in a single PDF file a short statement of your research experience and interests, a CV including a list of publications and the names and contact information of two possible references. Please feel free to contact Prof. Dr. Frank Glorius at any time, if you have additional questions.