The Department of Newborn Screening at Oslo University Hospital’s Division of Child and Adolescent Medicine is responsible for the provision of a nationwide newborn genetic screening programme in Norway, detecting 25 severe treatable disorders. The department is also responsible for quality assurance of the follow-up healthcare for these disorders.
The programme is organised as a central resource for the ~60,000 children born each year, and is obliged to both develop and quality control the service.
Over 700 genetic conditions are known to cause inborn errors of the metabolism as well as a group of immune deficiencies which are also known to have an underlying genetic cause. Newborn screening is working to identify new disorders that can be included in our panel using biochemical, immunological and genetic methods, as well as working towards a deeper understanding of disease severity, progression, and increasingly personalized treatment.
 
The department uses a wide selection of modern technologies which generate large volumes of data, and we are looking for a dynamic, capable, and enthusiastic PhD candidate to work with us on a 3 year project focused on the implementation of robust best-practice pipelines for quality control and interpretation of our newborn screening data. The PhD student will play a central scientific role in the implementation and development of rapid algorithms to accurately interpret biomarker profiles measured in the first tier newborn screen. These approaches will be contributed back to the international newborn screening community via the CLIR project (https://clir.mayo.edu/) at the Mayo Clinic.
 
This work will also provide a major contribution to understanding the capability to screen accurately for a wider range of disorders using complete newborn screening profiles, and will be closely tied to our extensive national and international collaborations.
 
Handling and interpreting newborn screening data will be a central function for the candidate, and the ability to contribute to the development and evolution of standards-based best-practice pipelines for analysis which are compatible with CLIR, will be highly important. In addition, familiarity with reproducibly handling, processing, visualising, and interpreting large multi-dimensional datasets will be necessary for this project.
Documented mathematical background and experience with data-analysis methods is required, for example (R, Python) and the candidate will be expected to be comfortable with the tools required to perform biostatistical analyses. Knowledge of  machine learning methods will also be an asset.