Postdoctoral Fellow in Physics-informed generative modelling of protein dynamics - Hiring in process/Finished, not possible to apply

The Department of Biochemistry and Biophysics. SciLifeLab (SciLifeLab) is a national center for molecular biosciences with focus on health and environmental research. The center combines frontline technical expertise with advanced knowledge of translational medicine and molecular bioscience. SciLifeLab is a national resource hosted by Karolinska Institutet, KTH Royal Institute of Technology, Stockholm University and Uppsala University. The center also collaborates with several other universities.

The employment will be placed at the Department of biochemistry and biophysics, at Stockholm university. More information about us, please visit: www.dbb.su.se.

Project description

The position will be associated with a project on physics-informed generative modelling of protein dynamics. The project aims to develop computational methods that move beyond prediction of single 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 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-aware protein modelling and Hessian-informed flow matching, and will be carried out in the interdisciplinary environment at SciLifeLab and the Department of Biochemistry and Biophysics.

Main responsibilities

The main responsibilities include:

• Develop, implement and evaluate machine-learning based models for protein dynamics and conformational ensembles.
• Generate, curate and analyse data from molecular dynamics simulations and structural ensembles, including flexibility, correlated motions and anisotropic motion descriptors.
• Develop physics-informed generative models, for example using flow matching and latent representations of conformational landscapes.
• Integrate and apply Markov state models, free-energy methods and Bayesian network models to analyse metastable states, transitions and allosteric couplings.
• Validate generated ensembles against simulations and, where possible, experimental information.
• Document code, data and workflows according to FAIR principles, publish results in scientific journals and present them at conferences.
• Contribute to the daily research environment through collaboration with group members, national and international partners and, to a limited extent, supervision of students.

Qualification requirements

In order to qualify for a Postdoctoral position, applicants are required to hold a Swedish doctoral degree or an equivalent relevant degree from another country. The degree must have been completed no later than when the employment decision is made.

Assessment criteria

It is considered an advantage if the doctoral degree or an equivalent degree was completed no more than three years prior to the application deadline. Under special circumstances, an older degree may also be an advantage. Special circumstances refer to sick leave, parental leave, elected positions in trade unions, service in the total defense, or other similar circumstances, as well as clinical attachment or service/assignments relevant to the subject area.

In the appointment process, special attention will be given to research skills and to the applicant’s ability to conduct independent research within the area of the project. Documented experience in computational biophysics, bioinformatics, computational chemistry, molecular dynamics simulations, enhanced sampling, free-energy methods, Markov state modelling or related methods for analysing protein dynamics is particularly meritorious.

Experience of machine learning, generative models, statistical mechanics, Bayesian network modelling or other physics-informed modelling is a strong merit. Excellent 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 consider publications, method development, ability to work independently and collaboratively, and strong written and oral communication skills in English.

About the employment

The position involves full-time employment for a minimum of two years and a maximum of three years, with the possibility of extension under special circumstances. Start date as soon as possible  or as per agreement.

We offer

With us, you will experience the dynamic interaction between higher education and research that makes Stockholm University an exciting and creative environment. You will work in an international environment and get favourable conditions. 

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

Contact

Further information about the position can be obtained from Professor Arne Elofsson, arne.elofsson@dbb.su.se.

Application

Apply for the 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.