We are a highly interdisciplinary research group sitting at the intersection of physics, biochemistry, and data science, and we actively welcome applications from applicants from any scientific background. Since we work in a highly indisciplinary area, you do not need to have experience in all areas of a project in order to apply, as all necessary training will be provided, and tailored to the applicants scientific background and skill set. However, most positions will require at least an interest in programming/statistics/data science.
For all enquiries, please contact email@example.com.
Postdocs and PhD positions
If you’re interested in working with us and we don’t have a position currently advertised, please contact us anyway. We are always interested in supporting fellowship applications from early-career researchers, so if you have an idea you want to discuss, and the possibilities available to you, drop us an email.
We welcome contact from Master’s students that are interested in doing projects within the group. We do not advertise specific Master’s projects, but we will always have several available, linked to our various research themes. If you have specific research interests, let us know and we will try to tailor the project to you.
We want this group to be a welcoming place for all, regardless of race, ethnicity, religion, nationaility, gender identity, or sexual orientation. We believe in enabling and supporting group members regardless of family commitments, age or disability. We hold ourselves to high standards and expect the same of those that work with us, so that we can build a welcoming, productive and collaborative work environment.
We currently have 3 advert(s) for positions in the group.
For most positions, we will consider candidates with backgrounds in e.g. data science interested in structural biology, and with backgrounds in structural biology interested in data science approaches. Please contact us if you have questions regarding eligibility, or to discuss a position!
I’m looking for a PhD student to develop statistical and machine learning approaches to analyze and improve modelling of disordered regions of macromolecular atomic models derived from multi-dataset X-ray crystallography experiments.
Working with both national and international experimental collaborators, you will apply these methods to study the role of flexibility and dynamics in relation to molecular recognition and function using data from time-resolved, multi-temperature and fragment screening crystallography experiments.
I’m looking for a postdoc to develop statistical and machine learning approaches to characterize and quantify flexibility in macromolecular atomic models determined by cryo-EM and crystallography experiments.
Using national Swedish & SciLifeLab computing infrastructure, you will apply these methods at scale to structures in the PDB, to build a publicly-accessible open database and study experimental flexibility in relation to molecular function. We will then use this database to explore macromolecular flexibility at scale.
I’m looking for a postdoc to use statistical and machine learning approaches to develop methods for improving modelling and refinement of heterogeneous regions of macromolecular atomic models derived from multi-dataset crystallography experiments.
You will use these approaches to generate highly detailed models that enable quantitative structural studies of multi-state systems, such as protein-ligand complexes obtained from fragment-screening experiments. You will engage with national & international collaborators to apply these methods to time-resolved and multi-temperature experimental data sets.