Vacant job

The Department of Molecular Biosciences, Wenner-Gren Institute (MBW) conducts experimental basic research in molecular cell biology, integrative biology, and infection and immunobiology. The research environment is characterized by modern and advanced methodology and has a strong international profile. MBW is one of the larger departments within the Faculty of Science at Stockholm University, with over 30 research groups and approximately 180 employees, of whom 65 are doctoral students. More information about us can be found at: Department of Molecular Biosciences, Wenner-Gren Institute (#).

Project Description

Proteomics-driven modeling of protein dynamics

We are looking for a highly motivated doctoral student for a DDLS-funded project at the intersection of structural proteomics, protein biophysics, and machine learning. The position is part of SciLifeLab and the doctoral school within the Wallenberg National Program for Data-Driven Life Science (DDLS), within the research area of Cell and Molecular Biology. The doctoral student will use and develop both computational and experimental methods.

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. SciLifeLab and the Wallenberg National Program for Data-Driven Life Science (DDLS) aim to recruit and train the next generation of data-driven life scientists and to build world-leading competence in computational and data science in Sweden. The program is funded with a total of 3.3 billion SEK over 12 years by the Knut and Alice Wallenberg Foundation.

See the full description on Stockholm University's job posting page.

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

The planned start date is October 2026.

The life sciences of the future are data-driven. Do you want to be part of shaping this development? You are welcome to become part of this unique program!

The dissertation work will be carried out within the framework of this project.

Qualifications

To be admitted to doctoral studies, the applicant must have basic and special eligibility. Eligibility must be met by the last day of application.

You have basic eligibility if you have completed an advanced level degree, or completed course requirements of at least 240 higher education credits (HEC), of which at least 60 HEC at advanced level, or in other ways within or outside the country acquired substantially equivalent knowledge.

Special eligibility is described in the general syllabus for doctoral studies in the subject.

Selection

Selection among applicants is made with regard to the ability to benefit from doctoral education. Criteria used to assess this ability are:

• Documented knowledge in relevant areas such as molecular biosciences, biochemistry, proteomics, structural biology, bioinformatics, computational biology, machine learning, or related areas.
• Analytical and creative thinking.
• Scientific curiosity and motivation for interdisciplinary research.
• Initiative and independence.
• Ability to collaborate in an international and interdisciplinary research environment.
• Good ability to express oneself orally and in writing in English.

The applicant should also have:

• A strong interest in protein science, proteomics, structural biology, computational biology, or machine learning.
• A solid background in molecular biology, biochemistry, bioinformatics, computational biology, computer science, physics, chemistry, or related areas.
• Motivation to work in an interdisciplinary project that links experimental biological data to computational modeling.
• Programming skills in Python, R, or other relevant programming languages.
• 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 meritorious but not required. Experience with the analysis of other types of -omics data is also valuable.
• Curiosity, analytical thinking, and willingness to learn new experimental and computational methods.

A strong interest in interdisciplinary research and the integration of experimental proteomics with data-driven modeling is crucial for the position.

Admission regulations for doctoral studies at Stockholm University.

About the Position

We offer a fixed-term position 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 in the employment, work with education, research, and administration may be included to a limited extent (maximum 20%).

A new appointment as a doctoral student is valid for up to one year, and the appointment is thereafter renewed for up to two years at a time.

Stockholm University strives to be a workplace free from discrimination and to provide equal rights and opportunities for all.

Contact

Further information is provided by Doctor Ilaria Piazza, [email protected].

Application

You apply for the doctoral position via Stockholm University's recruitment system. Attach a cover letter and CV, as well as the attachments requested in the application form. As an applicant, you are responsible for ensuring that the application is complete in accordance with the advertisement and that it is at the university's disposal by the last day of application.

Guidelines for applicants can be found on the webpage: applying for a position.

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