PhD student in Molecular Biosciences

Research at the Department of Molecular Biosciences, The Wenner-Gren Institute

Experimentally addresses fundamental problems in molecular cell biology, integrative biology, and infection and immunobiology. State-of-the-art and advanced methodologies are applied in a professional research environment characterized by its well-established international profile. The institute has 30 research groups with a research staff of 180, of which 65 are PhD students.

Proteomics-driven modeling of protein dynamics

We are seeking a highly motivated PhD student to join a DDLS-funded project at the interface of structural proteomics, protein biophysics, and machine learning. The position is part of the Sci Life Lab  and the research school of the Wallenberg National Program for Data-Driven Life Science (DDLS), within the research area Cell and Molecular Biology. To achieve this, the doctoral student will use and develop both computational and laboratory-based tools.

Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The Sci Life Lab  and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden.

The program is funded with a total of 3.3 billion SEK over 12 years from the Knut and Alice Wallenberg (KAW) Foundation.

In 2026 the DDLS Research School will be expanded with the recruitment of 25 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection.

For more information, please see Scilifelab.

Proteins are dynamic molecules whose biological functions are controlled not only by their folded structures, but also by conformational transitions induced by ligand binding, cofactors, metabolites, stress, or post-translational modifications. While recent deep-learning methods such as Alpha Fold have transformed protein structure prediction, most current models still describe proteins largely as static structures and do not fully capture the conformational ensembles that underlie protein function.

This PhD project aims to address this limitation by integrating experimental data with modern generative deep-learning models of protein conformational dynamics. The project will use structural proteomics methods that measures local protein accessibility and flexibility across thousands of proteins under native conditions. These experimental data will be used to guide, train, and benchmark machine-learning models that predict protein conformational states and structural ensembles.

The project builds on extensive expertise in the Piazza laboratory in quantitative proteomics, and proteome-wide analysis of protein structural changes. The PhD student will work closely with the group of Prof. Arne Elofsson at Stockholm University, who provides expertise in computational structural biology, protein modeling, and machine learning. The project therefore offers a unique interdisciplinary training environment bridging experimental mass spectrometry-based proteomics and AI-driven protein structure modeling.

The student will have access to existing large-scale datasets generated in the Piazza laboratory, public structural proteomics datasets, state-of-the-art mass spectrometry infrastructure at Sci Life Lab , and high-performance computing resources at Stockholm University, Sci Life Lab , and national Swedish infrastructure. The project is expected to lead to both methodological advances and biological insights into how protein conformational states are regulated in cells.

The PhD student will be part of a multidisciplinary and international research environment and will receive training in quantitative proteomics, structural biology, computational biology, machine learning, data integration, and scientific communication. As part of the DDLS Research School, the student will also participate in national courses, seminars, and networking activities within data-driven life science.

Expected starting date is October 2026.

The future of life science is data-driven. Will you be part of that change? Then join us in this unique program!

In order to be admitted to postgraduate education, the applicant must have the general and specific entry requirements. The qualification requirements must be met by the Final date to receive applications.

You meet general entry requirements if you have completed a second-cycle degree, or completed courses equivalent to at least 240 higher education credits, of which…