Postdoctoral Fellow in AI-Based Analysis of Submarine Glacial Landforms

The Department of Geological Sciences is a department within the Faculty of Science with courses at master's, bachelor's and orientation levels. The department supports a broad range of basic research within marine geology and geophysics, geochemistry and classical geology.

More information about us, please visit: the Department of Geological Sciences.

Project description

We are seeking a postdoctoral researcher for our interdisciplinary project aimed at developing and applying advanced machine learning techniques to recognize and classify submarine glacial landforms. This position is ideal for a scientist with expertise in machine learning and a strong interest in environmental or geoscientific applications. You will work with high-resolution seafloor terrain models, primarily collected via multibeam sonar systems, to develop robust computational tools for analyzing large geophysical datasets. You will:

• Apply advanced machine learning methods (e.g., supervised learning, clustering, neural networks) to classify submarine landforms.
• Collaborate with geomorphologists to ensure geological relevance and validate model results.
• Contribute to developing scalable tools for analyzing marine geophysical data, aiding predictions of glacial ice dynamics under climate change.

This role offers an exciting opportunity to merge cutting-edge computational techniques with pressing environmental research. This postdoctoral position is for two years, with the possibility of a one-year extension. It is part of a project funded by the Knut and Alice Wallenberg Foundation, which focuses on leveraging machine learning to interpret seafloor patterns and better understand the behavior of marine glaciers. The project involves collaboration with other postdoctoral scientists, PhD students, and faculty, as well as key international partners at the University of New Hampshire (USA), Scripps Institution of Oceanography (USA), and JAMSTEC (Japan). The Department of Geological Sciences hosts one of four regional centers within the Nippon Foundation-GEBCO Seabed 2030 project. Launched in 2017, Seabed 2030 aims to inspire ocean mapping and deliver a complete map of the world's ocean floor by 2030 for the benefit of humankind.

This initiative provides a unique opportunity for you to engage with a broad international network, apply cutting-edge machine learning methods to impactful environmental challenges, and contribute to advancing the science of seafloor mapping and climate research. 

Qualification requirements

Postdoctoral positions are appointed primarily for purposes of research. Applicants are expected to hold a Swedish doctoral degree or an equivalent degree from another country.

Assessment criteria

The degree must have been completed at latest before the employment decision is made, but no more than three years before the closing date. An older degree may be acceptable under special circumstances. Special reasons refer to sick leave, parental leave, elected positions in trade unions, service in the total defense, or other similar circumstances as well as clinical attachment or service/assignments relevant to the subject area.

We seek candidates with a strong background in computer science, geoscience, physics or applied mathematics with an interest to develop machine-learning algorithms to recognize and classify submarine glacial landforms using marine geophysical mapping data, primarily multibeam bathymetry. Documented previous experiences of development and application of machine learning algorithms are specifically meriting. Likewise, it is meriting to have documented experience in using High Performance Computing infrastructure. The candidate should be fluent in English, and be able to work independently and in groups, as well as towards deadlines as the project has specific sub-goals. We expect the candidate to specifically work with a postdoctoral scientist with a geoscience background employed on this project to identify, classify and interpret submarine glacial landforms. 

The selection among the eligible candidates is based on their expected capacity to complete the project tasks. The following criteria are used to assess this capacity: the applicant's documented knowledge and skills within the subject of their proposed specialization, capability to express their work orally and in writing in English, creativity, ability to take initiatives, capacity for analytical thinking, independence and ability to work with other researchers. The evaluation is based on previous education and experience, quality of previous published research, ambition expressed in the motivation letter, description of the proposed project direction and potentially interviews. 

About the employment

The position involves full-time employment for a minimum of two years and a maximum of three years, with the possibility of extension under special circumstances. Start date as per agreement.

We offer

With us, you will experience the dynamic interaction between higher education and research that makes Stockholm University an exciting and creative environment. You will work in an international environment and get favorable conditions. The university is located in the National City Park with good transport links to the city.

Stockholm University strives to be a workplace free from discrimination and with equal opportunities for all.

Contact

Further information about the position can be obtained from Professor Martin Jakobsson, martin.jakobsson@geo.su.se.

Application

Apply for the position at Stockholm University's recruitment system. Attach a personal letter and CV as well as the attachments requested in the application form. It is the responsibility of the applicant to ensure that the application is complete in accordance with the instructions in the job advertisement, and that it is submitted before the deadline. We recommend that you submit your application, including the necessary documents, in English.

The instructions for applicants are available at: How to apply for a position.