Vacant job

Department of Biochemistry and Biophysics. SciLifeLab is a national center for molecular biosciences focusing on research in health and the environment. The center combines leading technical expertise with advanced knowledge in translational medicine and molecular biosciences. SciLifeLab is a national resource operated by Karolinska Institutet, KTH, Stockholm University, and Uppsala University. The center collaborates with several other higher education institutions.

Administratively, the position is located at the Department of Biochemistry and Biophysics, Stockholm University. For more information about our activities, see our website: www.dbb.su.se (http://www.dbb.su.se).

Project Description

The position is linked to a project on physics-informed generative modeling of protein dynamics. The project aims to develop computational methods that go beyond predicting individual static protein structures and instead describe proteins as dynamic conformational ensembles.

The work will combine molecular dynamics simulations, generative machine learning, flow matching, prediction of flexibility and correlated motions, latent representations of free energy landscapes, Markov state models, and Bayesian network models.

The goal is to create models that can generate and interpret protein motions in a way that is consistent with underlying thermodynamics and free energy landscapes. The project builds on the research group's work in protein structure prediction, flexibility-driven protein modeling, and Hessian-informed flow matching, and is conducted in the interdisciplinary environment at SciLifeLab and the Department of Biochemistry and Biophysics.

Responsibilities

The responsibilities include:

• Developing, implementing, and evaluating machine learning-based models for protein dynamics and conformational ensembles.
• Generating, curating, and analyzing data from molecular dynamics simulations and structural ensembles, including flexibility, correlated motions, and anisotropic motion descriptions.
• Developing physics-informed generative models, such as using flow matching and latent representations of conformational landscapes.
• Integrating and using Markov state models, free energy methods, and Bayesian network models to analyze metastable states, transitions, and allosteric couplings.
• Validating generated ensembles against simulations and, where possible, experimental data.
• Documenting code, data, and workflows according to FAIR principles, as well as publishing results in scientific journals and presenting them at conferences.
• Contributing to the daily research environment through collaboration with group members, national and international partners, and, to some extent, supervising students.

Qualifications

To be eligible for the position of postdoctoral researcher, a PhD degree or a foreign degree assessed as equivalent to a relevant PhD is required. The degree must be completed no later than when the employment decision is made.

Selection Criteria

It is meritorious if the PhD or equivalent was awarded no more than three years before the last day of application. If there are special reasons, a degree awarded earlier may also be considered meritorious. Special reasons include leave due to illness, parental leave, trust positions within trade unions, service in the total defense, or other similar circumstances, as well as clinical service or service relevant to the subject area.

During the selection process, special emphasis will be placed on scientific competence and the applicant's ability to independently conduct research within the project's field. Documented experience in computational biophysics, bioinformatics, computational chemistry, molecular dynamics simulations, enhanced sampling, free energy methods, Markov state modeling, or related methods for analyzing protein dynamics is particularly meritorious.

Experience in machine learning, generative models, statistical mechanics, Bayesian network modeling, or other physics-informed modeling is highly meritorious. Very good programming skills, especially in Python and relevant packages for machine learning and molecular simulation, are meritorious. Experience with GROMACS, AMBER, MDAnalysis, PyEMMA, PyTorch, TensorFlow, SLURM, Singularity, or similar tools is an advantage.

The assessment will also take into account publications, method development, the ability to work independently and collaboratively, and good written and oral communication skills in English.

About the Position

The position is full-time and permanent, but for a minimum of two years and a maximum of three years, with the possibility of extension if there are special reasons. Start date as soon as possible or by agreement.

What We Offer

Here you will experience the dynamics of the interaction between higher education and research, making Stockholm University an exciting and creative environment. You will work in an international setting and enjoy favorable conditions.

Stockholm University strives to be a workplace free from discrimination, providing equal rights and opportunities for everyone.

Contact

Information about the position can be obtained from Professor Arne Elofsson, [email protected] (mailto:[email protected]).

Application

You apply for the 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 the application is complete and submitted to the university by the last day of application.

Instructions for applicants are available on the webpage: how to apply for a position.

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