Expert på maskininlärning för klimattillämpningar

Do you want to be involved in developing tomorrow's climate knowledge and a qualified decision basis for society and business? To meet society's expectations for information about future climates, Rossby Centre is now complementing its staff. We offer a stimulating environment with external contacts and flexible working hours.

The Rossby Centre is SMHI's climate modeling unit. Our main tools are the global and regional climate models developed within the research unit. Rossby Centre participates in national and international projects with research on climate processes, development of climate models and new forms of climate information. The research increases the understanding of the climate and is an important basis for climate services for societal sectors. Development of climate information and corresponding data is important for decision-making on adaptation measures in society and business.

About the job

We are now looking for a researcher with expertise in machine learning (ML) who, in close collaboration with climate experts at Rossby Centre, will further develop and apply machine learning methods for a range of application.

Central tasks are:


• to improve and apply ML-methods that are suitable for downscaling climate variables from climate models
• to develop and use ML-methods for the detection of weather and climate extremes
• to use ML-methods for climate forecasts
• to follow developments within ML and identify opportunities for application at SMHI, for example for tuning and spin-up of climate models or climate model ensemble extension.
• to write reports and scientific publications about results

The work will be performed initially as part of the ongoing Horizon EU project OptimESM (Optimal High-Resolution Earth System models for Exploring Future Climate Changes, https://optimesm-he.eu/ ), the Horizon EU project AI4PEX (Artificial Intelligence and Machine Learning for Enhanced Representation of Processes and Extremes in Earth System Models, with start in February 2024) and a Destination Earth project on ‘Machine Learning Generated Ensembles’. This will provide good opportunities to develop international cooperation, networks and communication.

Your profile

We are looking for you with an education from a university in mathematics, physics, computational science, climate science or a related field. A doctoral degree or comparable experience is a merit.

Documented experience of using ML-methods in climate or environmental research and experience with deep learning or convolutional neural networks are requirements. Further, you should understand the meaning of various alternative options of ML-methods and be capable to propose, develop or adapt existing deep learning topologies for the goal to emulate climate extreme events.

Expertise on application and development of ML-methods for downscaling of climate data, and competence in atmospheric or climate research or a similar field are merits, as well as scientific publications in the field.

You should be used to work with computer systems in a Linux environment with Python and handle large amounts of data. Knowledge of the programming language Fortran, C or shellscript, and to be able to perform calculations using GPUs are merits.

Personal qualities are of high importance. You should have analytical skills and the ability to plan and drive the work forward. You are responsible and appreciate collaborating with colleagues. A habit of delivering results on time with good quality is expected.

Good knowledge of English in both speech and writing is required. Knowledge in Swedish, Danish or Norwegian is a merit. If such knowledge is lacking, a willingness to learn Swedish is required. 

Form of employment:  Full time, permanent employment

Location: The location is at our office in Norrköping

Last day of Application: 2023-11-12

SMHI is an expert authority with a global outlook and a vital mission to forecast changes in weather, water and climate. With a scientific foundation, we use knowledge, research and services to contribute to a more sustainable society. Every hour of every day, all year round.

Bear in mind that all the documentation and information you send to SMHI in conjunction with your application will be classified as public documents. This means that all the material contained in your application, including attachments, may be disclosed if so requested by a party, on the condition that the information is not subject to confidentiality in accordance with the Public Access to Information and Secrecy Act. You should therefore primarily provide such information that you deem to be relevant in relation to the requirements of the position. Think about your own personal privacy, and avoid sharing information that contains sensitive personal data, or information about your health or that of a family member, your political views or religious beliefs.