The Leibniz Centre for Tropical Marine Research (ZMT) in Bremen is a member of the Leibniz Association, which is supported by the German Federal and State Governments. Through its research, ZMT contributes to developing strategies for sustainable use of tropical coastal systems.

The Data Science and Technology group at ZMT is looking for a Research Scientist to support research work on Scalable Habitat Mapping of Marine Ecosystems.

Marine ecosystems such as coral reefs and seagrass meadows have faced significant stresses due to rapid environmental and climate change. The need for better assessments of marine and freshwater ecosystems has therefore grown stronger, and an understanding of the dynamics and trajectory of these vital ecosystems is limited by the ability to describe the habitat structure in detail. It is essential to develop new methods, technologies and capacities to enhance the quality of habitat mapping and description of coastal ecosystems across geographical regions.

The Data Science and Technology group seeks to develop efforts towards scalable habitat mapping of marine and coastal ecosystems. Through a combination of techniques from data science, computer vision and machine learning, the aim is to develop new scales of analytical throughput for producing habitat maps of coral reefs, mangrove/kelp forests and others. The work will involve several local and international collaborations to produce new scales of observation and analysis for spatial ecology and modelling.

Candidates interested in research work at the intersection of marine ecology, artificial intelligence, and Earth observation are invited to apply.

Scope of position:

The Research Scientist position will be based in the Data Science and Technology group.

For any queries about the position, please contact Dr. Arjun Chennu (arjun.chennu@leibniz-zmt.de).

The position is expected to involve:

  • Development of collaborative and innovative research goals towards improved analytical throughput of habitat mapping for marine and coastal surveys
  • Machine learning and photogrammetric modelling of (color or multi-spectral) images from aerial and underwater surveys
  • Development of methods and mapping products useful for spatial ecological study of systems such as coral reefs

Candidate profile:

The candidate is expected to have:

  • A PhD degree and scientific experience in fields of marine sciences, computer vision, artificial intelligence or similar
  • Strong skills in software programming to develop custom analyses for large datasets
  • Demonstrable experience with relevant techniques in image analysis, machine learning and data fusion
  • Curiosity and motivation for topics of marine science and habitat mapping
  • Strong interest and abilities to work in an interdisciplinary and collaborative setting
  • Excellent scientific writing skills and proven record of scientific publishing

We offer:

  • An exciting working environment in an interdisciplinary and internationally oriented institute and research project
  • Possibilities to build and extend an international professional network and participate in a research field of high public and scientific interest
  • A family-friendly working environment

The selected candidate is expected to start in February 2022. The contract will initially be limited to 24 months. The position is full-time and salary will be paid according to the German TV-L EG13 tariff. ZMT is an equal opportunity employer. Applicants with a migration background are welcome. Disabled persons with comparable qualification receive preferential status.

Please send your application (your scientific CV, including a list of publications, a letter of motivation (max 2 pages), a primer of relevant experience (max 1 page), contact details (email and phone) of two references, copies of relevant certificates) until December, 8th, 2021 as a single pdf file with the reference number “216 – DIGIZ” to Ms. Lena Oehlmann, E-Mail: bewerbung@leibniz-zmt.de.

For more information about the position, please follow this link.

via ZMT
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