Ecotoxicological modeller in coordinated research project

Society of Environmental Toxicology and Chemistry; Essen; Vor Ort; EUR 38.560 - 54.807; Vollzeit

Zusammenfassung

The University Duisburg-Essen (UDE) invites applications for a Doctoral researcher in Ecotoxicological Modelling (f/m/d) at Campus Essen within the SFB RESIST. Salary classification TV-L 13, 65%, gross salary €38,560–€54,807.

The RESIST Collaborative Research Centre investigates how stressors individually and in combination affect biodiversity and ecosystem functions in streams and rivers. The doctoral researcher will develop mechanistic models, analyse data, and contribute to publications while

Qualifikationen

• Above-average 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.

Aufgaben

• 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.

Kenntnisse

Mechanistic modelling

Population ecology

Ecotoxicology

Data analysis

Team collaboration

Ausbildung

Master's degree in environmental sciences or related quantitative field

Tools

R

Python

Jobbeschreibung

The University Duisburg-Essen (UDE) is seeking a Doctoral researcher in Ecotoxicological Modelling (f/m/d) at the Campus Essen in the SFB RESIST. Salary classification 13 TV-L, 65%. Gross salary €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 examines the effects of stressors on all components of the food web of running waters (from viruses to fish) and on ecosystem functions. 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 degradation and recovery scenarios.

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.