Research at the Department of Molecular Biosciences, The Wenner-Gren Institute (MBW) 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. More information about us, please visit: Department of Molecular Biosciences, The Wenner-Gren Institute.
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 SciLifeLab 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 SciLifeLab 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 AlphaFold 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 SciLifeLab, and high-performance computing resources at Stockholm University, SciLifeLab, 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 deadline for 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 60 credits must be in the second cycle, or have otherwise acquired equivalent knowledge in Sweden or elsewhere.
Specific entry requirements are described in the general syllabus for doctoral studies in the field.
The selection among the eligible candidates will be based on their capacity to benefit from the training. The following criteria will be used to assess this capacity:
Documented knowledge in a relevant field, such as molecular biosciences, biochemistry, proteomics, structural biology, bioinformatics, computational biology, machine learning, or related areas.
Capacity for analytical and creative thinking.
Scientific curiosity and motivation for interdisciplinary research.
Initiative and independence.
Ability to collaborate in an international and cross-disciplinary research environment.
Written and oral proficiency in English.
The candidate should have:
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A strong interest in protein science, proteomics, structural biology, computational biology, or machine learning.
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A solid background in molecular biology, biochemistry, bioinformatics, computational biology, data science, physics, chemistry, or a related discipline.
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Motivation to work in an interdisciplinary project connecting experimental biological data with computational modeling.
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Programming skills in Python, R, or another relevant language.
Interest in machine learning, statistical modeling, structural bioinformatics, or analysis of large-scale biological datasets. -
Experience with proteomics, mass spectrometry, protein structure analysis, molecular dynamics, deep learning, or bioinformatics is advantageous but not required. Experience with data analysis of other -omics data would be valuable.
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Curiosity, analytical thinking, and willingness to learn new experimental and computational approaches.
A strong interest in interdisciplinary research and in the integration of experimental proteomics with data-driven modeling is essential for this position.
Admission Regulations for Doctoral Studies at Stockholm University.
We offer a fixed-term employment as a doctoral student according to Chapter 5 of the Higher Education Ordinance (1993:100). The period of employment may not be longer than what corresponds to full-time doctoral education for four years. As a doctoral student, you should primarily devote yourself to your own doctoral education, but the employment may include work with education, research and administration to a limited extent (maximum 20 %).
A new employment as a doctoral student is for a maximum of one year, the employment is then renewed for a maximum of two years at a time.
Stockholm University strives to be a workplace free from discrimination and with equal opportunities for all.
For more information, please contact Doctor Ilaira Piazza, [email protected]
Apply for the PhD student position at Stockholm University's recruitment system. Attach a personal letter and CV as well as the attachments requested in the application form. It is the responsibility of the applicant to ensure that the application is complete in accordance with the instructions in the job advertisement, and that it is submitted before the deadline.
The instructions for applicants are available at: How to apply for a position.
Stockholm University contributes to the development of sustainable democratic society through knowledge, enlightenment and the pursuit of truth.
Anställningsform: tidsbegränsad anställning | Anställningens omfattning: heltid | Antal lediga befattningar: 1 | Sysselsättningsgrad: 100 % | Ort: Stockholm | Län: Stockholms län | Land: Sweden | Referensnummer: SU FV-1940-26 | Facklig företrädare: ST/OFR ST/OFR 08162000, ST/OFR ST/OFR 08162000, Saco-S Saco-S 08162000, Saco-S Saco-S 08162000, Seko Seko 0770457900, Seko Seko 0770457900, | Publicerat: 2026-06-11 | Sista ansökningsdag: 2026-07-13
