Scientific software developer and statistician for climate model emulation at ETH Zürich, Switzerland

The Land-Climate Dynamics group (LCD), led by Prof. Sonia I. Seneviratne at the Institute for Atmospheric and Climate Science (IAC) focuses on land-climate interactions and feedback as well as changes in climate extremes with research streams emphasizing climate modelling, observational assessments, and process analysis. As part of these research activities, the group is actively developing MESMER, which is designed to approximate Earth System Models using statistical and data-driven methods (e.g. Beusch et al. 2020, Beusch et al. 2022, Quilcaille et al. 2022, Quilcaille et al. preprint, GitHub). MESMER generates global time series fields of climate variables, including indicators of climate extremes and impacts, at yearly to monthly resolution with low computational cost. Currently, MEMSER is at the backbone of several national and international (EU) projects including SPEED2ZERO, PROVIDE, and SPARCCLE. Over the last years, MESMER has grown organically with inputs from a wide community and is now undergoing a re-structuring and harmonization process to ensure a stable and user-friendly scientific software.

They are looking for a colleague who is eager to develop and advance MESMER on a technical and theoretical level. Tasks will include the integration of recent developments into the main repository, improvement of MESMER’s data handling capabilities, and the advancement beyond the current restructuring to version 1.0. The successful candidate will therefore work closely with and support the scientific and technical MESMER developers in the LCD group and external stakeholders. In addition, there will be ample opportunity to shape and develop the statistical framework used in MESMER.

• An MSc or PhD degree in computer science, climate/environmental science, statistics, physics, applied math, or in a related field
• A strong interest in weather and climate research
• An excellent knowledge in the Python programming language as well as Git and, optimally, a track record of open-source software development
• A passion about using quantitative methods with a focus on probabilistic spatio-temporal statistics or deep generative methods
• A creative personality and enjoy solving technical and scientific challenges independently as well as in team efforts
• A will to create and document high-quality scientific software and to support users in the application thereof
• A good command of spoken and written English

Full job announcement

Deadline: 1st October 2023