This exciting postdoctoral position at Stockholm University focuses on leveraging machine learning (ML) to identify errors in large bathymetric datasets and applying ML techniques like "super-resolution" to enhance acquired bathymetry data.

Project description

As our dependence on the oceans grows and the health of the oceans becomes threatened, knowledge of the depth of the seafloor (bathymetry) is essential for understanding climate dynamics, marine ecosystems, resource management, risk managements and much more.

Recognizing the critical importance of seafloor mapping, the Nippon Foundation-GEBCO Seabed 2030 project was launched in 2017 with the goal of inspiring ocean mapping and delivering a complete map of the world's ocean floor by the year 2030 for the benefit of humankind. While the available high-resolution seafloor mapping data has increased, covering about 6% of the world's oceans in 2017 and almost 25% in 2023, new technologies and approaches will be needed to help fill the remaining 75% of the seafloor that has not yet been directly mapped.

This postdoctoral position at Stockholm University focuses on leveraging machine learning (ML) to identify errors in large bathymetric datasets and applying ML techniques like "super-resolution" to enhance acquired bathymetry data.

The postdoctoral position is for two years, with a potential extension to three years. It is hosted at Stockholm University, and the project includes key collaborators at the University of New Hampshire (USA), Scripps Institution of Oceanography (USA), and JAMSTEC (Japan) as well as at all partner institutes involved in the Seabed 2030 project. The position is funded by The Ocean Policy Research Institute of the Sasakawa Peace Foundation.

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.

The university seeks candidates with a strong background in computer science, geoscience, physics or applied mathematics with an interest to develop machine-learning algorithms to identify errors in large bathymetric datasets and enhance acquired bathymetry data.

Apply by 1st of March 2024.