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

Doctoral Research Associate (Wissenschaftlicher Mitarbeiterin, salary level E 13 TV-L)

**at the earliest possible date, preferably by 1st April 2026. This is a full position (100%) limited to 3 years. This PhD position is available within the EU-funded Marie Skłodowska Curie Doctoral Network on Low Data Machine Learning for Sustainable Chemical Sciences under Grant Agreement No. 101226058.

As 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. This project is a collaboration between 13 academic and industrial organizations 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.

Project tasks:

We propose a data science guided and ML driven screening and optimization 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. For this purpose, both direct substructure matching 3D similarity searches will be performed to determine key reactions based on their product motifs. To be a useful tool for the synthesis of diverse compounds, however, the generality of a methodology is also critical to allow the incorporation of a variety of substrates and functional groups. The Glorius group has previously developed an additive based screening approach for the assessment of a chemical reactions’ functional group compatibility that will be used to rapidly evaluate the generality of key reactions. Key reactions showing a promising generality profile will then be subjected to a reoptimization process. This is necessary since synthetic methods are mostly optimized with respect to a single substrate and not broad applicability. Since the number of possible reaction conditions, especially for catalytic reactions, does not allow bruteforce exploration it is important to select the most informative experiments. Bayesian optimization, an iterative global optimization algorithm, has been shown to be a more efficient in optimizing reaction conditions compared to human decision making and design of experiments techniques. We will develop a Bayesian optimization algorithm for the optimization of reaction yields for representative substrates. As running multiple experiments in parallel during each optimization step will greatly reduce the evaluation time and experimental effort, a batch selection strategy will be implemented to efficiently explore the search space. The reactions proposed by the optimizer 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 liquid and gas chromatography in combination with UV, mass and flame ionization detection. The optimized key reactions will be applied in the multi-step diversity-oriented synthesis of druglike compounds.

Expected Results

1. Identification of underrepresented structural motifs accessible by modern synthetic methodologies.
2. Assessment of the robustness of key reactions using an additive based screening approach.
3. Development of a multi-objective Bayesian optimization algorithm for the reoptimization of reactions.
4. Application of the reoptimized key reaction in the synthesis of drug-like compounds.**

Your tasks:

As a PhD student in this Doctoral network you will use machine learning (ML) for the optimization of 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 tool for the optimization of reaction yields should be developed. The reactions proposed by the optimizer 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.The PhD candidate will have research stays at

• Farm-ID (iMed Lisboa, Portugal, a highly regarded research-oriented institution in Pharmaceutical Sciences - 4 months, extension of their expertise in ML-based reaction optimization
• AZ (AstraZeneca, Sweden) – 4 months, investigate the optimization approaches’ utility in real-world applications and the key reactions integrability in retrosynthesis tools.
• AC (Acceleration Consortium (AC) is a University of Toronto (UofT) initiative, Canada) – 4 months, integrate the ML tools in SDL technologies

Our expectations:

You are excited to work at the intersection between chemistry, AI, and software development. Working in an interdisciplinary team between scientists & 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 degree and/or Bachelor’s degree and must not have resided or carried out their main activity (work, studies, etc.) in the host country (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 are values are important to us.
• Whether you need care or childcare - our Family Service Office offers you specific support services to help you balance your private and professional life.
• Your individual, customized training and further education is not only important to us as an educational institution, but a matter close to our hearts.
• From Aikido to Zumba - our sports and health programs from A to Z ensure your work-life balance.
• You will benefit from numerous 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 . 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.

Universität Münster
Organisch-Chemisches Institut
Prof. Dr. Frank Glorius
Corrensstraße 40, 48149 Münster
Phone: 0251 83 35345, Email:

Reference number: 2025_12_29