Description
The University Duisburg-Essen (UDE) is seeking at the Campus Essen in the SFB RESIST
Doctoral researcher in Ecotoxicological Modelling (f/m/d)
(salary classification 13 TV-L, 65 %)
(brutto 38.560 € - 54.807 €)
The RESIST Collaborative Research Centre investigates how stressors individually and in combination affect the biodiversity and functions of streams and rivers, and how previously stressed ecosystems recover. RESIST investigates the effects of stressors on all components of the food web of running waters (from viruses to fish) and on ecosystem functions. Field studies, large collaborative mesocosm experiments, a wide range of analytical (especially genomic) and modelling methods are used. The doctoral researcher will be embedded in Project A18, which investigates how stressor effects at the individual level scale up to population and community responses. The project combines empirical analyses, meta-analyses, and mechanistic modelling to quantify the role of physiological tolerance, dispersal, and biotic interactions (Asymmetric Response Concept, ARC) under both degradation and recovery scenarios.
Application
Please send your electronic application with the usual documents, quoting reference number 324-26, to Prof. Dr. Bernd Sures, University of Duisburg-Essen, Department Aquatic Ecology, 45141 Essen, Email: bernd.sures@uni-due.de
Requirements
Your tasks
- Develop and apply mechanistic models to investigate stressor effects from individuals to populations and communities.
- Analyse and synthesize ecological and ecotoxicological data, including meta-analytical approaches.
- Evaluate the relative importance of key ecological processes (e.g. physiological tolerance, dispersal, biotic interactions) using modelling and empirical data.
- Contribute to scientific publications and interdisciplinary collaboration within the Coordinated Research Centre
Your profile
- Above-average university master’s degree in environmental sciences, ecology, ecotoxicology, physics, applied mathematics, or a related quantitative field.
- Strong interest in mechanistic modelling of ecological or ecotoxicological processes.
- Experience with programming (preferably R; Python or similar advantageous).
- Background in population and community ecology, ecological modelling, or environmental stress biology is desirable.
- Experience with meta-analysis, statistical modelling, or handling large datasets is advantageous.
- Excellent command of written and spoken English.
- High degree of self-initiative, analytical thinking, creativity, and ability to work collaboratively.
- The successful candidate is expected to pursue a cumulative PhD within the framework of the project.
