Tenure-track Position in Computational Aquatic Ecology (m/f/x)

The Department of Plankton and Microbial Ecology at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) invites applications for the position of a data scientist to strengthen our research on the structure and functioning of freshwaters and their responses to global environmental change. Located on a large clear-water lake one hour north of the German capital, Berlin, the department is fully equipped for field and lab work and stands out by its long-term data series on lakes, cutting-edge tools for data acquisition, and a unique lake enclosure facility.
We look forward to recruiting a creative scientist focusing on the analysis of large data sets to advance understanding of freshwater ecosystems and predictions of responses to environmental change. This involves close collaboration with multiple data contributors and other scientists at IGB as well as engagement in international networks. Assets facilitating this research include comprehensive long-term data sets on lakes, experimental data acquired during large-scale enclosure experiments, and high-frequency data collected in situ during both experiments and long-term observations.

Your opportunities
• Establishment of an independent group developing an innovative research program
• Focus on comprehensive long-term and experimental data sets on lakes, which include high-resolution and high-frequency data
• Engagement in collaborative research, including in international initiatives such as GLEON
• Further development of lake monitoring programs, data management and quality control

Your profile
• Doctoral degree in quantitative ecology, biostatistics, aquatic or environmental sciences, geophysics, or a related field
• Profound expertise in data analyses with advanced statistics
• Programming skills (e.g. Python, R, C++, SQL)
• Interest in developing A.I. approaches
• Excitement to collaborate in a team of aquatic scientists with diverse backgrounds
• Preferably experience in working with large data sets and in database management
• Strong publication track record commensurate with career stage

Apply here

Deadline 06.01.2023

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