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The (SDSC) is a joint venture between EPFL and ETH Zurich. Its mission is to accelerate the adoption of data science and machine learning techniques within the ETH Domain academic disciplines, the Swiss academic community at large, and the industrial sector. In particular, it addresses the gap between those who create data, those who develop data analytics and systems, and those who could potentially extract value from it. The centre comprises a multi-disciplinary team of data and computer scientists, and experts in select domains with offices in Zürich, Lausanne, and Villigen.
We are seeking a postdoc to work on the SNF-funded N2O-SSA project as well as on related SDSC research projects. The postdoc will be based at the Swiss Data Science Centre in Zürich and will work closely with the Department of Environmental Systems Science - in particular the Sustainable Agroecosystems Group (https://sae.ethz.ch/) - as well as the University of Eldoret, Kenya ().
The position is available at 80-100% FTE for 3-4 years, ideally starting in early 2023.
N2O-SSA: Combining measurements, modelling and machine learning to improve N2O accounting for sustainable agricultural development in sub-Saharan Africa
N2O is a strong greenhouse gas emitted during microbial N cycling in soils, particularly N-fertilised agricultural soils. Fertiliser use in sub-Saharan Africa is currently relatively low, and increased fertilisation to drive higher agricultural productivity is predicted in the coming decades. There is great potential for climate-smart agricultural techniques in this region to ensure food security while minimising environmental impacts and greenhouse gas emissions. However, financial investment in sustainable agriculture in sub-Saharan Africa is very low, in part due to the scarcity of data, in particular regarding N2O fluxes and emission factors. This project aims to develop and implement a novel measurement technique to monitor N2O fluxes and isotopic composition, and thus infer N2O production and consumption pathways for the first time in sub-Saharan Africa. We will investigate various computational approaches to simulate N2O fluxes across sub-Saharan Africa and understand drivers and causes of uncertainty, with a particular focus on causal inference. We will develop an optimised modelling framework to allow robust predictions of future N2O emissions in a changing climate, to contribute to closing the data gap and fostering sustainable agricultural development in this region.
Further information about the project can be found on the website:
This project will be divided into four parts: i) Development and testing of a coupled chamber-preconcentration- isotopic measurement system in Switzerland, including automated data analysis software to facilitate rapid quality checking and troubleshooting; ii) Deployment of the system at an experimental field site in Eldoret, Kenya, to monitor N2O flux and isotopic composition through a dry-wet seasonal transition for two crop types and two fertilisation levels, in close collaboration with project partners at the University of Eldoret.
The first two parts of the project will be conducted mainly by the project PhD student, with guidance and collaboration with the postdoc. The advertised postdoctoral position focuses on the third and fourth parts of the project: iii) The application of machine learning to improve current N2O flux models, in particular IsoTONE (https://doi.org/10.1038/s41467-022-32001-z), to characterise uncertainty and improve computational efficiency, and thus allow robust predictions in a range of conditions; and iv) To investigate causal relationships between environmental parameters and N2O fluxes in tropical soils, and thus understand drivers of variability in N2O production pathways, using techniques such as causal invariance algorithms and a customized flux generative model for robust predictions of N2O.
This interdisciplinary postdoc is a unique combination of biogeoscience and machine learning methodologies, thus offering a unique development platform for the postdoctoral researcher.
The wider impact of this project will result from model development, which will provide new, robust data for N2O fluxes and emission factors in sub-Saharan Africa, and efficient modelling tools that can be used to investigate the impacts of climate change as well as management and mitigation policies on emissions. Furthermore, the project will strongly contribute to education and capacity building, strengthening ties between research communities in Europe and sub-Saharan Africa.
The SDSC works on research projects together with scientists from a diverse range of fields within the ETHZ and EPFL domain (). In addition to N2O-SSA, the postdoc will have the opportunity to work with SDSC data scientists on 1-2 ongoing SDSC projects, in particular:
You must have a PhD in biogeochemistry, environmental sciences, machine learning, mathematics, or a related subject, and experience with advanced data analysis using python, R or similar. Ideally, you have conducted interdisciplinary work employing data science approaches in environmental sciences, or you have a strong interest in developing this profile.
Regardless of your scientific background, you must be motivated to work in a challenging, cutting-edge research field, to work independently and develop your ideas, and collaborate with others from a range of backgrounds. Fluency in English (written and spoken) and strong communication skills, in particular experience in presentation and publication of research results at conferences and in peer-reviewed journals, is required.
We value diversity: In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.
We look forward to receiving your online application with the following documents:
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered. The initial application deadline is October 14, 2022, but a review of applications will continue until the position is filled.
Further information about the Swiss Data Science Center can be found on our website ().
Questions regarding the position should be directed via email to email@example.com.
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