Post doc in Biochemistry - Hiring in process/Finished, not possible to apply

Work description

We are looking for a highly motivated candidate with strong experience of probabilistic modeling, to run a project where Bayesian statistical methods are developed to analyze experimental data interrogating the three-dimensional structure of proteins.  

A central goal in this project is to increase our understanding of how proteins self-assemble to form larger structures using time-resolved experimental data, primarily from small angle scattering (SAS). The candidate will develop Bayesian approaches to infer kinetic models of self-assembly processes from the experimental data. The project will also involve the development of other statistical inference methods coupled to the analysis of SAS data, and additional methods to investigate the structure of mixtures of proteins in solution.  

The position does not require a background in protein science or scattering methods, but candidates interested in applying probabilistic modeling to problems in chemistry and physics are encouraged to apply. 

The project is highly interdisciplinary and involves collaboration with researchers from Lund University, Chalmers University and European Spallation Source.

Qualifications

Mandatory:

• A PhD in statistics, computer science, machine learning, bioinformatics, biophysics or other relevant fields
• A strong background in probabilistic methods, with a good understanding of Bayesian statistics
• Good programming skills and experience of writing programs or advanced scripts in the area of probabilistic modeling
• Be fluent in English (written and spoken)

Meriting:

• Background in one or more of the following languages: C++, C, Matlab, Python, R
• Background with Bayesian graphical networks
• Background in protein modeling
• Background in small angle scattering