Statistician for Complex Environmental Data Analyses

The Jülich Supercomputing Centre (JSC)

Jülich, Germany

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As a member of the Helmholtz Association, Forschungszentrum Jülich makes an effective contribution to solving major challenges facing society in the fields of information, energy, and bioeconomy. It focuses on varied tasks in the area of research management and utilizes large, often unique, scientific infrastructure. Come and work with around 6,100 colleagues across a range of topics and disciplines at one of Europe's largest research centres.

The Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich operates one of the most powerful supercomputer infrastructures for scientific and technical applications in Europe and grants scientists in Germany and Europe access to these resources for their research. The department of Federated Systems and Data (FSD) at JSC develops software for data analysis, data management and workflow management on distributed data and computer systems. The research group Earth System Data Exploration within FSD applies these tools to develop new analysis methods for Earth System data with a focus on air quality and weather data. Specifically, with the ERC Advanced Grant IntelliAQ, a large part of the group focuses on developing and evaluating deep learning methods for air quality assessments. In this context we operate the Tropospheric Ozone Assessment Report (TOAR) data center, one of the largest collections of air quality data in the world. In support of the next TOAR assessment we would like to strengthen our capabilities to perform statistical analyses of air quality variability and trends, assess the quality of air pollutant measurement series, and evaluate the power of the deep learning methods in comparison to well-established statistical techniques.

We are looking to recruit a

Statistician for Complex Environmental Data Analyses

Your Job:

  • design and implement new advanced statistical methods to assess the variability and trends of global air pollution data and the representativeness of air pollution measurements in the TOAR database
  • provide statistical expertise for the development of an automated quality control tool for the TOAR database (AutoQC4Env) and assist in the implementation of statistical tests in the AutoQC4Env tool
  • develop and implement evaluation concepts for testing the power and robustness of the deep learning methods that are developed in the ESDE group
  • assist ESDE group members in statistical aspects of environmental data analysis

Your Profile:

  • master's with subsequent doctoral degree from a university with internationally accepted quality standards in mathematics, physics, or environmental sciences with a solid training in advanced statistics
  • very strong command of statistical methods that are used in the context of environmental data analysis (timeseries analysis, statistical modelling, ARIMA, Generalized Additive Mixed Models, Gaussian processes, hypothesis testing, Bayesian methods)
  • experience in software development with R, Python, C++, or C
  • willingness to keep abreast of new developments in statistics and data science
  • strong team player with very good command of the English language

Our Offer:

  • opportunity to work on interesting research questions with high societal relevance in an open and dedicated team
  • excellent research and computing infrastructures in one of Europe’s largest research facilities
  • a comprehensive further training programme
  • flexible working hours and various opportunities to reconcile work and private life
  • limited until 30.09.2023 with possible longer-term prospects
  • full-time position with the option of slightly reduced working hours
  • salary and social benefits in conformity with the provisions of the Collective Agreement for the Civil Service (TVöD)

Forschungszentrum Jülich promotes equal opportunities and diversity in its employment relations.

The job will be advertised until the position has been successfully filled. You should therefore submit your application as soon as possible. We look forward to receiving your application via our Online-Recruitment-System!

Questions about the vacancy?
Contact us by mentioning the reference number 2019-456: career@fz-juelich.de Please note that for technical reasons we cannot accept applications via email.

www.fz-juelich.de


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