Postdoc in Systems Biology (focusing on federated analysis of COVID-19 clinical data)

The EU project ORCHESTRA provides an innovative approach to learn from the COVID-19 crisis and derive recommendations to be better prepared for future pandemics. The ORCHESTRA project aims to deliver scientific evidence for the prevention and treatment of infections, taking into account not only epidemiological, clinical, microbiological, and genotypic aspects of the population, but also environmental and socio-economic factors. The project builds up on existing and new large scale population cohorts in Europe (France, Germany, Spain, Italy, Belgium, Romania, Netherlands, Portugal, Luxemburg, and Slovakia) and non-European countries (India, Peru, Ecuador, Colombia, Venezuela, Argentina, Brazil, Congo, and Gabon) composed by SARS-CoV-2 infected and non-infected individuals of all ages and conditions. The main contribution of ORCHESTRA will be the creation of a new pan-European cohort applying homogenous protocols for data collection, data sharing, sampling, and follow-up, which can rapidly advance the knowledge on the control and management of COVID-19. 

Carrying out the analysis of such a large-scale, multi-center data set requires overcoming several challenges. The most limiting factor is the legal impossibility of sharing and collecting in a single location all the data due to privacy concerns. We will utilize federated learning techniques, which allow the statistical analysis of distributed data without individual-level information ever being directly accessible to the analyst. In contrast to standard meta-analysis techniques, the results of a federated analysis are equivalent (or practically so) to a pooled analysis. Due to its novelty, algorithms and software for federated learning are still few. We will thus need to take known methods from machine learning and statistical analysis, as well as (fine-grained) dynamical models, and adapt them to a federated framework.

Job description:

    (Federated) statistical analysis and machine learning for the analysis of a large-scale, multi-center COVID-19 data set in order to study factors relevant to the infection and disease severity for acute- and long-COVID

    Interpretation of analysis results

    Collaboration with biologists and medical researchers

    Publication of scientific results at conferences and in journals

    Assistance with teaching and training (e.g., in mathematics or computer sciences)

    Co-supervision of students

Your profile:

    PhD degree in (bio-)informatics, computational biology, computer science, mathematics or equivalent

    Strong experience in some of the following topics: Bioinformatics, statistics, mathematical modeling (e.g., ODEs and PDEs for biological processes), machine learning, and data management/integration

    Programming skills (preferably R and/or Python)

    Proficiency in written and spoken English

    Passion for science and scientific work

Our offer:

    Working in an innovative, well-equipped and scientifically stimulating environment 

    An international and diverse group of PhD students and Postdocs

    A professional career development program

    Opportunities to obtain additional external funding and develop an independent research program during postdoctoral training.

    Initial fixed-term employment contract for 2 years with a standard public service salary (PostDoc: 100% TV EntgO Bund EG 13)

The University of Bonn is committed to diversity and equal opportunity. It is certified as a family friendly university. It aims to increase the proportion of women in areas where women are under-represented and to promote their careers in particular. It therefore urges women with relevant qualifications to apply. Applications will be handled in accordance with the Landesgleichstellungsgesetz (State Equality Act). Applications from individuals with a certified serious disability and those of equal status are particularly welcome.

The deadline for the application round is March 15, 2022.

Application documents (cover letter, CV, certificates, contact details of two referees) should be submitted as soon as possible as a single PDF file via email.

Contact: Prof. Dr. Jan Hasenauer, [email protected]