We are looking for an experienced post-doctoral fellow with a strong interest in health-related data science and proven expertise in genome wide association modelling to join the Birney and Gerstung research groups at EMBL-EBI as part of a Novo Nordisk funded consortium - Challenge.
Denmark has an extremely advanced electronic heath care system allowing large scale linking of complex human traits to genetic variation. The data sets available within the Challenge project provide linkable health related and socioeconomic information to genome wide variation data allowing truly unique combined risk modelling opportunities. This project provides direct linkage across a diverse set of human traits with detailed genetic information. We are looking for a highly motivated computational geneticist or bioinformatician to join this exciting project, and to drive specific aspects of this cutting-edge research, including the analysis of complex human trait measurements using modern genetic association techniques.
The integration of genetic variation with complex human measurements is a key area of this work, you will be able to explain detailed reasoning behind model parameter choices across a wide range of traits. Additionally, there is a strong interest in combined modelling and the successful candidate will be able to explain the general principles behind, for example, polygenic risk score modelling. An overall ability to display data science nous and genetic modelling expertise will be highly beneficial.
The post holder needs to hold a PhD in Statistical genetics, Bioinformatics or related fields and have demonstrable skills in data science and the analysis of large-scale genetic data sets. You should have solid experience working with complex data, have a good knowledge of current machine learning as well as being skilled in developing software in a Unix-based environment.
The successful applicant will join a consortium working towards a collective goal of using electronic health care records in combination with genetic datasets to understand biology and human health. Good interpersonal skills are required to interact with consortium members across a wide range of disciplines. Detailed technical knowledge, being able to setup quickly to work on large scale and diverse compute infrastructure is essential.