Postdoc in Machine Learning for Oligopeptide Design (1.0 FTE)

The advent of modern machine learning (ML) methodology is accelerating scientific progress by streamlining research across disciplines. In chemistry, it enables the modeling of complex structure-function relationships with unprecedented accuracy. Additionally, deep generative models allow for the generation of structures with desired function, thus enabling the design of novel molecules and materials.

Peptide-based materials provide attractive alternatives to materials derived from petrochemicals. By tuning the peptide sequence and modifications, material properties can be finely tuned. Their behavior at surfaces and interfaces are among the most fundamental, but also most important properties of small peptides. Control over these materials can enable a new generation of biodegradable materials, surfactants and coatings. However, how peptide sequence and modification affect the physicochemical properties and material performance remains unclear, let alone inverse design of these molecules.

The goal of this project is to develop ML models for: 1) predicting properties of oligopeptide materials based on peptide sequence and end-group functionalization; 2) guiding experimental structure optimization workflows to minimize the number of experiments to achieve desired properties; 3) generating new peptide sequences with desired target properties.
This position will be part of the 10 (https://bigchemistry.nl/), which combines laboratory automation with artificial intelligence. By building an autonomous ‘RobotLab’, large numbers of experiments can be carried out, yielding large datasets on properties of molecular systems. With this data, ML models are trained to predict the properties of molecular systems such as solubility, phase separation, critical micelle concentration, smell, toxicity, and reaction rates.

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.

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

• A PhD degree in either machine learning or computational molecular sciences.
• Advanced knowledge in molecular machine learning.
• Advanced knowledge in computational chemistry.
• Advanced programming skills and experience with scientific software development.
• 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 may apply for this position until 28 February 11:59 pm/ before 29 February 2025 Dutch local time (CET), by means of the application form (click on the link "Apply" below, in the advertisement). Applications will be assessed on a rolling basis.

Applications for the postdoc position should include:

• Curriculum vitae (name, country of residence, degree(s) with transcript(s) of grades, research experience, educational and employment history).
• List of publications.
• Motivation letter.
• Name and email address of two professional references.
• Vacancy number of this position.

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. For more information, see also our diversity policy webpage: https://www.rug.nl/(...)rsity-and-inclusion/

Our selection procedure follows the guidelines of the Recruitment code (NVP): https://www.nvp-hrnetwerk.nl/nl/sollicitatiecode and European Commission's European Code of Conduct for recruitment of researchers: https://euraxess.ec.europa.eu/jobs/charter/code

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