Equal opportunities are an integral part of our personnel policy; we therefore particularly welcome applications from qualified women. Severely disabled persons are given priority where applicants are equally qualified.

Your contact for any questions you may have about the job:
Prof. Dr. Andreas Huth

Prof. Dr. Sabine Attinger

Closing date for applications:

Place of work: Leipzig

Please use our online application system for your application.

More information about jobs at the UFZ:

Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
Permoserstraße 15
04318 Leipzig, Germany

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The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.

Discovering relevant patterns in large volumes of data and extracting useful information for further application is an increasingly important problem in today´s data rich world.
The majority of users of complex computer models in the environmental sciences face the same challenge: the transformation / condensation of the huge amount of simulation data into knowledge. At the very core of this transformation process three components have been identified: uncertainty quantification, development of fast surrogate models and the identification of key parameters and dependencies. Within an interdisciplinary Helmholtz project “Reduced Complexity Models – explore advanced data science techniques to create models of reduced complexity“, several Helmholtz centers aim in addressing and developing the three components simultaneously and in a unified manner. The UFZ is therefore seeking for a

Postdoc (m/f)

Subject: Development of aggregated ecological models with reduced complexity

Earliest start: 01.09.2018, Working time: 100% (39 hours per week), limited to 2 years

Your tasks:

Typically, science uses partial and/or ordinary differential equation based mathematical models to describe processes in ecology like vegetation growth or competition between species. Often, vegetation models (e.g. forest model FORMIND) are more complex and include a larger number of parameters. The resulting computer code is huge and expensive in terms of the computer time. Individuals may be modelled not explicitly but aggregated into species or species groups and only the number of species or individuals of functional classes are modelled. These simpler aggregated models have the capability to substitute the (expensive) computer model. The accuracy of an aggregated model needs to be verified, e.g. by design augmentation.

The successful candidate will hence:

  • develop data-­driven aggregated models (for vegetation models) by function approximation techniques in combination with scaling and stochastic methods
  • develop copula-based techniques to establish the optimal causation and dependence predictor network
Competing aggregated models will be evaluated with Bayesian Model evidence techniques to identify the most robust one.

Your profile:

  • PhD in physics, mathematics or ecological modelling
  • experience with dynamic ecological models
  • strong expertise in mathematics and programming (e.g. C++)
  • knowledge in High Performance Computing
  • good knowledge in data science and Bayesian methods
  • excellent English skills
  • motivated to work in a highly interdisciplinary team

We offer:

  • Excellent technical facilities which are without parallel
  • The freedom you need to bridge the difficult gap between basic research and close to being ready for application 
  • Work in inter-disciplinary, multinational teams and excellent links with national and international research networks
  • A vibrant region with a high quality of life and a wide cultural offering for a balance between family and professional life
  • Interesting career opportunities and an extensive range of training and further education courses
  • Remuneration up to TVöD public-sector pay grade 14 including attractive public-sector social security benefits