Postdoctoral Position in Phytoplankton Analysis and Modelling

WorkplaceDübendorf, Zurich region, Switzerland


Eawag, the Swiss Federal Institute of Aquatic Science and Technology, is an internationally networked aquatic research institute within the ETH Domain (Swiss Federal Institutes of Technology). Eawag conducts research, education and expert consulting to achieve the dual goals of meeting direct human needs for water and maintaining the function and integrity of aquatic ecosystems.

Phytoplankton Ecology at Eawag (Dübendorf), in collaboration with Aquatic Physics (Eawag, Kastanienbaum) and the Institute of Earth Sciences (SUPSI, Lugano), opens a

The goal of the projectis to study the combination of environmental factors that favour dominance of toxic and bloom forming cyanobacteria in deep peri-alpine Swiss lakes and assess, through a data-driven model and a sensitivity analysis, the effect of projected changes in climate and nutrient loadings on future cyanobacterial abundances.

We are looking for a highly motivated person with interests in quantitative computational ecology to integrate field data with aquatic ecosystem science. At least 90% of the work will be computational, while the remaining 10% can be dedicated to fieldwork (depending on the candidate’s interests). The postdoc will take a leading role in the project, bridging across groups and disciplines, and will have the opportunity to developed new ideas and studies based on the wealth of data available to the group.

The successful candidate has an earned Ph.D and is required to have demonstrated skills in statistical analyses and/or computational modelling, computer programming (e.g. R, MATLAB and/or Python), and scientific communication (including paper and report writing). Fluency in English and a strong interest in phytoplankton ecology are also required. Experience with forecasting methodologies particularly as they apply to ecological problems would be desirable. Other desired skills include experience in machine learning, aquatic / microbial ecology (either marine or freshwater), analysis and modelling of ecological data series, large data sets, familiarity with cyanobacterial ecology and lake ecosystems.

The selected candidate will (1) explore the use of machine learning and other data-exploration approaches to study drivers of bloom-forming cyanobacterial abundance using monitoring data from 10 Swiss lakes; (2) generate scenarios of lake change based on climate and nutrient levels projections; (3) forecast abundances of bloom forming cyanobacteria at different time scales based on the input scenarios and the data driven model.

The duration of the position will be 2.5 years, with a possibility of extension to 3 years pending on additional funding. The candidate will be based in the group of Phytoplankton Ecology at Eawag in Dübendorf, supervised by Dr Francesco Pomati . The candidate will work part time at Eawag Kastanienbaum and at SUPSI (University of Applied Sciences and Arts of Southern Switzerland, Lugano), collaborating with various groups at Eawag, ETH-Zürich and EPFL-Lausanne, Denmark, Germany, Canada and USA.

Eawag offers a unique research and working environment and is committed to promoting equal opportunities for women and men and to support the compatibility of family and work. Applications from women are especially welcome. For more information about Eawag and our work conditions please consult ?" target="_blank" rel="nofollow"> and ?" target="_blank" rel="nofollow"> . Eawag is located within the Zürich metropolitan area, and the city of Zürich is continuously ranked among the top cities in the world for quality of life and is within close proximity to the Swiss Alps.

Applications must be submitted by April 29, 2018 and should include a concise research statement (max 2 pages) and your motivation to work on this project (max 1 page), a curriculum vitae including the list of publications, copies of your academic qualifications, and names and contact information of 2-3 academic references (please do not include letters with the application).

For further information, please contact Dr Francesco Pomati, Email francesco.pomati [at] eawag[.]ch.

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