Thursday, July 20, 2017

Postdoc, (Biological/Paleontological Data), The Natural History Museum, University of Oslo, Norway

Closing Date: 31 October 2017

A two-year position as a research fellow in Machine Reading Approaches Applied to Biological/Paleontological Data is available at the Natural History Museum, University of Oslo, in association with the Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, Faculty of Mathematics and Natural Sciences,University of Oslo, Norway.

The research fellow will be part of the European Research Council Consolidator Grant project (Abiota, biota and constraints in macroevolutionary processes) lead by Associate Professor Lee Hsiang Liow (PI). The main goal of this project is to develop a new model system using bryozoans to provide answers to previously intractable questions in evolutionary biology.

The Natural History Museum and the Centre for Ecological and Evolutionary Synthesis
The position will be based at the Natural History Museum (NHM), University of Oslo ( with affiliation to CEES (, a national centre of excellence (CoE). The Abel computer cluster at the Faculty of Mathematics and Natural Sciences that provides High-Performance Computation (HPC) abilities will be available for use. The appointed researcher will carry out research at the Natural History Museum at its location in the Botanical Garden at Tøyen and at the Blindern campus of the University of Oslo, and potentially at the University of Wisconsin-Madison.

The candidate will work in closely with the PI and Shanan Peters (University of Wisconsin-Madison) who is the project leader of GeoDeepDive (

Project Description
The main goal of the project is to develop a new model system using bryozoans to provide answers to previously intractable questions in evolutionary biology. These questions include whether ecological interactions that are important for individual survival matter for speciation and extinction patterns observed on very long time scales, and why we can expect to wait a million years for bursts of phenotypic change. The research fellow has the designated task of developing automated data recognition and extraction software tools to help locate and aggregate “dark” occurrence, ecological and phenotypic data on bryozoans that are distributed in publications, scientific reports and other published sources, in an automated and repeatable fashion. The ideal candidate will make a major contribution to the planned research, but will also develop her/his own complementary lines of research that reflect the candidate's own expertise and interests, and that fit within the project's main goals, such as developing data-extraction algorithms and software tools to automate scientific workflows that depend on locating and using published information. The Natural History Museum has an ambition of being a leading research museum. Candidates for researcher fellowships will be selected in accordance with this, and are expected to be in the upper segment of their class with respect to academic credentials.

Applicants should hold a PhD-degree (or other corresponding education equivalent to a Norwegian doctoral degree). In exceptional cases, candidates without a doctoral degree but with highly relevant (and demonstrable) skills in machine reading approaches, and a very strong interest in bryozoans and/or macroevolution will be considered. We are seeking a motivated, enthusiastic and hard-working researcher who can communicate with systematists, biologists and palaeontologists, with the ambition of helping them streamline repeatable high-volume data extraction. The data to be extracted includes text, numbers and potentially also attributes of images. The ideal candidate has substantial experience with programming and in particular machine reading/learning, and interests in database construction. She/he should have excellent communication and writing skills to foster understanding across fields. Applicants must show good interpersonal skills and be willing to work in close collaboration with the project PI and other members of the project team, as well as have the ability to work independently. Applicants should have a good publication record for their career stage and/or a solid record of successful machine-learning/reading projects. A good command of English is required. Please also refer to the regulations pertaining to the conditions of employment:

The application must include:
  • Application letter including a statement of interest, briefly summarizing your scientific work and interests and describing how you fit the description of the person we seek. A 1-2 page description of how, at this early stage, you envision potentially using GeoDeepDive infrastructure to locate and extract taxonomic descriptions, faunal lists and other relevant biological information.
  • An example of such an extraction activity is available here:
  • CV (summarizing education, positions, and other qualifying activities).
  • Copies of educational certificates.
  • A complete list of publications and unpublished work, and up to 5 academic papers that the applicant wishes to be considered by the evaluation committee.
  • Names and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number). Foreign applicants are advised to attach an explanation of their University’s grading system. Please remember that all documents should be in English or a Scandinavian language.
If you have any questions regarding the application procedure or would like to know more about the project, please do not hesitate to contact Assoc. Prof. Lee Hsiang Liow, e-mail:

For questions regarding the recruitment system Jobbnorge or the application procedure, please contact HR Advisor Thomas Brånå, e-mail:

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